DAS Data Processing to Identify Fluid Inflow Locations and Fluid Type

ABSTRACT

A method of identifying inflow locations along a wellbore includes obtaining an acoustic signal from a sensor within the wellbore, determining a plurality of frequency domain features from the acoustic signal, and identifying, using a plurality of fluid flow models, a presence of at least one of a gas phase inflow, an aqueous phase inflow, or a hydrocarbon liquid phase inflow at one or more fluid flow locations. The acoustic signal includes acoustic samples across a portion of a depth of the wellbore, and the plurality of frequency domain features are obtained across a plurality of depth intervals within the portion of the depth of the wellbore. Each fluid flow model of the plurality of fluid inflow models uses one or more frequency domain features of the plurality of the frequency domain features, and at least two of the plurality of fluid flow models are different.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and priority to InternationalApplication No. PCT/EP2018/082985 filed Nov. 29, 2018 with the EuropeanReceiving office and entitled “DAS Data Processing to Identify FluidInflow Locations and Fluid Type” as a foreign priority claim, where suchapplication is hereby incorporated herein by reference in its entiretyfor all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

BACKGROUND

Within a hydrocarbon production well, various fluids such ashydrocarbons, water, gas, and the like can be produced from theformation into the wellbore. The production of the fluid can result inthe movement of the fluids in various downhole regions, including withinthe subterranean formation, from the formation into the wellbore, andwithin the wellbore itself. For example, some subterranean formationscan release water that can be produced along with the hydrocarbons intothe wellbore. Such water inflow can cause a number of problems includingerosion, clogging of wells due to resulting sand inflow, contaminationand damage of the surface equipment, and the like. Significant waterproduction can result in the need to choke back production from the wellto bring water production down to acceptable levels. This can lead toreduced oil production, and potentially result in a deferral ofsubstantial amounts of the production from the well.

Efforts have been made to detect the movement of various fluidsincluding hydrocarbon liquids, water, and gas within the wellbore. Forexample, a production logging system utilizing a Production LoggingSystem (PLS) can be employed to determine flow profile in wells. A PLScan be utilized to assess what fluids (oil/water/gas) are present in awell at a given depth, where there is inflow, and what fluid isinflowing. A PLS can also provide data regarding what the flow rate ofinflowing fluid is and the flow regime (e.g., slug flow, bubble flow,etc.).

A typical PLS utilizes capacitive and resistive sensors to assesswhether the inflowing fluid comprises oil, water, or gas, and radiallyfacing “spinners” to measure an inflow rate. The sensors can bedistributed around the circumference of the PLS so that the fluidprofile and inflow rate can be assessed circumferentially. Thus,information on the background flow profile, inflow profile, backgroundflow rate and inflow flow rate and flow regime can be obtained with aPLS.

When utilizing a PLS, measurements are recorded for a depth at the frontof the PLS tool. Since the PLS tool can be ten to twenty meters long,and the sensors are distributed along the length of the PLS, sensorsthat are not at the front of the tool are not actually takingmeasurements at the depth for which the measurements are recorded. Thus,the data at times can be skewed by variability in flow regime caused bythe intrusive nature of the measurement. Further, the flow can bealtered by the presence of the PLS tool, such that what is measured atthe downstream end of the tool may not be indicative of what the flowprofile or flow regime was before the tool disturbed the flow.Furthermore, a PLS tool is typically run through a well once or a fewtimes (down and then up once or a few times and then out of the well),so the sensors of the PLS are exposed to the conditions at a given depthfor only a brief period of time. The PLS log is established based onthat brief window of data, at a given moment in the life of the well,but may be used for the many (e.g., five or ten) years due to the highcost of running a PLS tool into a well. Fluid characteristics within awell can change substantially over that time as the well ages, and/or afluid may flow into a well erratically (off and on). For example, thePLS may detect the presence of gas at a time when there is gas inflow ata certain depth, but that gas inflow may fluctuate significantly,sometimes even over the course of a few hours. Nevertheless, futuredecisions about the well may be based on the assumption that there isalways that same amount of gas present. Thus, the use of PLSs has anumber of limitations.

Accordingly, a need exists for systems and methods of determining fluidinflow locations and type of fluids that are inflowing in adynamic/continuous distributed fashion. Desirably, such systems andmethods also enable a determination of the relative amounts of thedifferent fluids or fluid phases (e.g., gas, water, hydrocarbon liquid)that are inflowing.

BRIEF SUMMARY OF THE DISCLOSURE

In an embodiment, a method of identifying inflow locations along awellbore comprises obtaining an acoustic signal from a sensor within thewellbore, wherein the acoustic signal comprises acoustic samples acrossa portion of a depth of the wellbore, determining a plurality offrequency domain features from the acoustic signal, wherein theplurality of frequency domain features are obtained across a pluralityof depth intervals within the portion of the depth of the wellbore, andidentifying at least one of a gas phase inflow, an aqueous phase inflow,or a hydrocarbon liquid phase inflow using the plurality of thefrequency domain features at one or more fluid inflow locations. In someembodiments, the plurality of frequency domain features can comprise atleast two different frequency domain features.

In an embodiment, a method of developing an inflow location model for awellbore comprises performing a plurality of inflow tests, wherein eachinflow test comprises introducing one or more fluids of a plurality offluids into a conduit at predetermined locations, and wherein theplurality of fluids comprise a hydrocarbon gas, a hydrocarbon liquid, anaqueous fluid, or a combination thereof, obtaining an acoustic signalfrom a sensor within the conduit for each inflow test of the pluralityof inflow tests, wherein the acoustic signal comprises acoustic samplesacross a portion of the conduit including the predetermined locations,determining one or more frequency domain features from the acousticsignal for each test, wherein the one or more frequency domain featuresare obtained across the portion of the conduit including thepredetermined locations, and training a fluid flow model using the oneor more frequency domain features for a plurality of the tests and thepredetermined locations. The inflow test can introduce the one or morefluids into a flowing fluid in some embodiments.

In an embodiment, a method of characterizing fluid inflow into awellbore comprises obtaining an acoustic signal from a sensor within thewellbore, wherein the acoustic signal comprises acoustic samples acrossa portion of a depth of the wellbore, determining a plurality offrequency domain features from the acoustic signal, wherein theplurality of frequency domain features are obtained across a pluralityof depth intervals within the portion of the depth of the wellbore, andwherein the plurality of frequency domain features comprise at least twodifferent frequency domain features, identifying one or more fluidinflow locations within the plurality of depth intervals using one ormore frequency domain features of the plurality of frequency domainfeatures, providing the plurality of frequency domain features at theidentified one or more fluid inflow locations to a fluid flow model, anddetermining at least one of a gas phase inflow, an aqueous phase inflow,or a hydrocarbon liquid phase inflow at the identified one or more fluidinflow locations using the fluid flow model.

In an embodiment, a method of identifying inflow locations along awellbore comprises obtaining an acoustic signal from a sensor within thewellbore, wherein the acoustic signal comprises acoustic samples acrossa portion of a depth of the wellbore, determining one or more frequencydomain features from the acoustic signal, wherein the one or morefrequency domain features are obtained across a plurality of depthintervals within the portion of the depth of the wellbore, andidentifying one or more fluid inflow locations within the plurality ofdepth intervals using the one or more frequency domain features.

These and other features will be more clearly understood from thefollowing detailed description taken in conjunction with theaccompanying drawings and claims.

Embodiments described herein comprise a combination of features andadvantages intended to address various shortcomings associated withcertain prior devices, systems, and methods. The foregoing has outlinedrather broadly the features and technical advantages of the invention inorder that the detailed description of the invention that follows may bebetter understood. The various characteristics described above, as wellas other features, will be readily apparent to those skilled in the artupon reading the following detailed description, and by referring to theaccompanying drawings. It should be appreciated by those skilled in theart that the conception and the specific embodiments disclosed may bereadily utilized as a basis for modifying or designing other structuresfor carrying out the same purposes of the invention. It should also berealized by those skilled in the art that such equivalent constructionsdo not depart from the spirit and scope of the invention as set forth inthe appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a detailed description of the preferred embodiments of theinvention, reference will now be made to the accompanying drawings inwhich:

FIG. 1 is a flow diagram of a method of identifying fluid inflowaccording to embodiments of this disclosure;

FIG. 2 is a schematic, cross-sectional illustration of a downholewellbore environment according to an embodiment of this disclosure;

FIGS. 3A and 3B are a schematic, cross-sectional views of embodiments ofa well with a wellbore tubular having an optical fiber associatedtherewith;

FIG. 4 is a schematic view of an embodiment of a wellbore tubular withfluid inflow according to an embodiment of this disclosure;

FIG. 5 is an exemplary frequency filtered acoustic intensity graphversus time over five frequency bands;

FIG. 6 illustrates an embodiment of a schematic processing flow for anacoustic signal, according to an embodiment of this disclosure;

FIG. 7 is a flow diagram of a method of developing a fluid flow modelaccording to embodiments of this disclosure;

FIG. 8A is a schematic illustration of a flow loop assembly utilized totrain an inflow model, according to embodiments of this disclosure;

FIG. 8B is a schematic showing wellbore depths corresponding toinjection points of FIG. 8A;

FIG. 9 is a generic representation of possible outputs producedaccording to embodiments of this disclosure; and

FIG. 10 schematically illustrates a computer that can be used to carryout various steps according to an embodiment of this disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Unless otherwise specified, any use of any form of the terms “connect,”“engage,” “couple,” “attach,” or any other term describing aninteraction between elements is not meant to limit the interaction todirect interaction between the elements and may also include indirectinteraction between the elements described. In the following discussionand in the claims, the terms “including” and “comprising” are used in anopen-ended fashion, and thus should be interpreted to mean “including,but not limited to . . . ”. Reference to up or down will be made forpurposes of description with “up,” “upper,” “upward,” “upstream,” or“above” meaning toward the surface of the wellbore and with “down,”“lower,” “downward,” “downstream,” or “below” meaning toward theterminal end of the well, regardless of the wellbore orientation.Reference to inner or outer will be made for purposes of descriptionwith “in,” “inner,” or “inward” meaning towards the central longitudinalaxis of the wellbore and/or wellbore tubular, and “out,” “outer,” or“outward” meaning towards the wellbore wall. As used herein, the term“longitudinal” or “longitudinally” refers to an axis substantiallyaligned with the central axis of the wellbore tubular, and “radial” or“radially” refer to a direction perpendicular to the longitudinal axis.The various characteristics mentioned above, as well as other featuresand characteristics described in more detail below, will be readilyapparent to those skilled in the art with the aid of this disclosureupon reading the following detailed description of the embodiments, andby referring to the accompanying drawings.

As utilized herein, a ‘fluid inflow event’ includes fluid inflow (e.g.,any fluid inflow regardless of composition thereof), gas phase inflow,aqueous phase inflow, and/or hydrocarbon phase inflow. The fluid cancomprise other components such as solid particulate matter in someembodiments, as discussed in more detail herein.

Disclosed herein is a new signal processing architecture that allows forthe identification of fluid inflow locations, fluid inflowdiscrimination in real time or near real time, and fluid flowdiscrimination within a conduit such as a wellbore. As utilized herein,“fluid flow discrimination” indicates an identification and/orassignment of the detected fluid flow (e.g., single phase flow, mixedphase flows, time-based slugging, altering fluid flows, etc.), gasinflow, hydrocarbon liquid (e.g., ‘oil’) inflow, and/or aqueous phase(e.g., water) inflow, including any combined or multiphase flows orinflows. The methods of this disclosure can thus be utilized to provideinformation on various flow events such as a fluid ingress point as wellas flow regimes within a conduit rather than simply a location at whichgas, water, or hydrocarbon liquid is present in the wellbore tubular(e.g., present in a flowing fluid), which can occur at any point abovethe ingress location as the fluid flows to the surface of the wellbore.In some embodiments, the system allows for a quantitative measurement ofvarious fluid flows such as a relative concentration of in-wellhydrocarbon liquid, water, and gas.

In some instances, the systems and methods can provide information inreal time or near real time. As used herein, the term “real time” refersto a time that takes into account various communication and latencydelays within a system, and can include actions taken within about tenseconds, within about thirty seconds, within about a minute, withinabout five minutes, or within about ten minutes of the action occurring.Various sensors (e.g., distributed fiber optic acoustic sensors, etc.)can be used to obtain an acoustic sampling at various points along thewellbore. The acoustic sample can then be processed using signalprocessing architecture with various feature extraction techniques(e.g., spectral feature extraction techniques) to obtain a measure ofone or more frequency domain features and/or combinations thereof thatenable selectively extracting the acoustic signals of interest frombackground noise and consequently aiding in improving the accuracy ofthe identification of the movement of fluids (e.g., gas inflowlocations, water inflow locations, hydrocarbon liquid inflow locations,etc.) in real time. While discussed in terms of being real time in someinstances, the data can also be analyzed at a later time at the samelocation and/or a displaced location.

As used herein, various frequency domain features can be obtained fromthe acoustic signal, and in some contexts, the frequency domain featurescan also be referred to herein as spectral features or spectraldescriptors. In some embodiments, the spectral features can compriseother features, including those in the time domain, various transforms(e.g., wavelets, Fourier transforms, etc.), and/or those derived fromportions of the acoustic signal or other sensor inputs. Such otherfeatures can be used on their own or in combination with one or morefrequency domain features, including in the development oftransformations of the features, as described in more detail herein. Thesignal processing techniques described herein can also help to addressthe big-data problem through intelligent extraction of data (rather thancrude decimation techniques) to considerably reduce real time datavolumes at the collection and processing site (e.g., by over 100 times,over 500 times, or over 1000 times, or over 10,000 times reduction, Insome embodiments).

In some embodiments, the acoustic signal(s) can be obtained in a mannerthat allows for a signal to be obtained along the entire wellbore or aportion of interest. As noted hereinabove, production logging systemsutilize a production logging system (PLS) to determine flow profile inwells. However, since the PLS can be 10-20 meters long and the sensorsare distributed along the length, sensors that are not at the front ofthe PLS are not actually taking measurements at the depth for which themeasurements are recorded, and, thus, the data can be incorrect orincomplete over time. Furthermore, the flow can be altered by the merepresence of the PLS within the wellbore, so what is measured at thedownstream end of the PLS is not an accurate reflection of what theprofile/regime was before the tool disturbed the flow. Furthermore, as aPLS is typically run through a well once or a few times (down and thenup once or a few times and out), and the sensors are exposed to theconditions at a given depth for only a very brief period of time (e.g.,4-5 seconds). Accordingly, while PLSs can provide an indication thatcertain events, such as downhole water inflow, may be occurring, they donot provide continuous measurements over prolonged durations of timethat would be needed to study dynamic variabilities in productionprofiles over time.

Fiber optic distributed acoustic sensors (DAS) capture acoustic signalsresulting from downhole events such as gas inflow/flow, hydrocarbonliquid inflow/flow, water inflow/flow, mixed flow, and the like, as wellas other background acoustics. This allows for signal processingprocedures that distinguish fluid inflow and flow signals from othernoise sources to properly identify each type of event. This in turnresults in a need for a clearer understanding of the acousticfingerprint of in-well event of interest (e.g., fluid inflow, waterinflow, gas inflow, hydrocarbon liquid inflow, fluid flow along thetubulars, etc.) in order to be able to segregate and identify a noiseresulting from an event of interest from other ambient acousticbackground noise. As used herein, the resulting acoustic fingerprint ofa particular event can also be referred to as a spectral signature, asdescribed in more detail herein.

Further, reducing deferrals resulting from one or more events such aswater ingress and facilitating effective remediation relies uponaccurate and timely decision support to inform the operator of theevents. Heretofore, there has been no technology/signal processing forDAS that successfully distinguishes and extracts fluid inflow locations,let alone in near real time.

The ability to identify various fluid inflow events in the wellbore mayallow for various actions to be taken in response to the events. Forexample, a well can be shut in, production can be increased ordecreased, and/or remedial measures can be taken in the wellbore, asappropriate based on the identified event(s). An effective response,when needed, benefits not just from a binary yes/no output of anidentification of in-well events but also from a measure of relativeamount of fluids (e.g., amount of gas inflow, amount of hydrocarbonliquid inflow, amount of water inflow, etc.) from each of the identifiedzones of fluid inflow so that zones contributing the greatest fluidamount(s) can be acted upon first to improve or optimize production. Thesystems and methods described herein can be used to identify the sourceof the problem, a direction and amount of flow, and/or an identificationof the type of problem being faced. For example, when a water inflowlocation is detected, a relative flow rate of the hydrocarbon liquid atthe water inflow location may allow for a determination of whether ornot to remediate, the type or method of remediation, the timing forremediation, and/or deciding to alter (e.g., reduce) a production ratefrom the well. For example, production zones can be isolated, productionassemblies can be open, closed, or choked at various levels, side wellscan be drilled or isolated, and the like. Such determinations can beused to improve on the drawdown of the well while reducing theproduction expenses associated with various factors such as producedwater.

Herein described are methods and systems for identifying fluid inflowlocations and/or fluid flow regimes within a conduit in the wellbore. Asdescribed herein, spectral descriptors can be used with DAS acousticdata processing to provide for downhole fluid profiling, such as fluidinflow location detection and fluid phase discrimination (e.g., thedetermination that the fluid at one or more locations such as thedetected fluid inflow location comprises gas inflow, hydrocarbon liquidinflow, aqueous phase inflow, a combined fluid flow, and/or a timevarying fluid flow such as slugging single or multiphase flow). In someembodiments, a fluid flow model can be used for inflow fluid phasediscrimination to determine at least one of a gas phase inflow, anaqueous phase inflow, a hydrocarbon liquid phase inflow, and variouscombinational flow regimes. In some embodiments, the same or a differentfluid flow model can be used for fluid flow phase discrimination todetermine the composition of fluid flowing in a conduit. A method ofdeveloping a suitable fluid inflow/flow model is also provided herein.

Application of the signal processing techniques and fluid flow modelwith DAS for downhole surveillance can provide a number of benefitsincluding improving reservoir recovery by monitoring efficient drainageof reserves through downhole fluid surveillance (e.g., production flowmonitoring), improving well operating envelopes through identificationof drawdown levels (e.g., gas, water, etc.), facilitating targetedremedial action for efficient well management and well integrity,reducing operational risk through the clear identification of anomaliesand/or failures in well barrier elements.

In some embodiments, use of the systems and methods described herein mayprovide knowledge of the zones contributing to fluid inflow and theirrelative concentrations, thereby potentially allowing for improvedremediation actions based on the processing results. The methods andsystems disclosed herein can also provide information on the variabilityof the amount of fluid inflow being produced by the different fluidinflux zones as a function of different production rates, differentproduction chokes, and downhole pressure conditions, thereby enablingcontrol of fluid inflow. Embodiments of the systems and methodsdisclosed herein also allow for a computation of the relativeconcentrations of fluid ingress (e.g., relative amounts of gas,hydrocarbon liquid, and water in the inflow fluid) into the wellbore,thereby offering the potential for more targeted and effectiveremediation.

As disclosed herein, embodiments of the data processing techniques use asequence of real time digital signal processing steps to identify theacoustic signal resulting from fluid inflow from background noise, andallow real time detection of downhole fluid inflow zones usingdistributed fiber optic acoustic sensor data as the input data feed.

As disclosed herein, a model can be developed using test data toidentify one or more signatures based on features of the test data andone or more machine learning techniques to develop correlations for thepresence of various flow and/or inflow regimes using the signatures. Inthe inflow model development, specific flow regimes can be introducedinto a test set-up and the acoustic signals obtained and recorded todevelop test data. The test data can be used to train one or more modelsdefining the various flow and inflow regimes. The resulting model canthen be used to determine one or more inflow and/or flow regimes withinthe wellbore. Use of the models are described initially, and the processand systems for developing the models used to identify the flow regimesare described in more detail herein.

Referring now to FIG. 1, a flow chart of a method I of identifying fluidflow and/or inflow according to some embodiments of this disclosure isshown. As described herein, the methods and systems can be used toidentify fluid flow. As used herein fluid flow can comprise fluid flowalong or within a tubular within the wellbore such as fluid flow withina production tubular. Fluid flow can also comprise fluid flow from thereservoir or formation into a wellbore tubular. Such flow into thewellbore and/or a wellbore tubular can be referred to as fluid inflow.While fluid inflow may be separately identified at times in thisdisclosure, such fluid inflow is considered a part of fluid flow withinthe wellbore.

A method of identifying fluid flow and/or inflow can comprise obtainingan acoustic signal along the wellbore at 100 and determining one or aplurality of frequency domain features from the acoustic signal at 300.In some embodiments, the method includes identifying one or more fluidinflow locations at 500. In some embodiments, the method includesdetermining fluid inflow discrimination, and the method can also includeidentifying at least one of a gas phase inflow, an aqueous phase inflow,or a hydrocarbon liquid phase inflow at one or more fluid inflowlocations using the plurality of frequency domain features at 600. Whenused to identify flow regimes, the method can include identifying atleast one of a gas phase flow, an aqueous phase flow, and/or ahydrocarbon liquid phase flow at one or more locations in the wellbore.

As depicted in the embodiment of FIG. 1, a method of identifying fluidflow and/or inflow according to this disclosure can includepreprocessing the acoustic signal at 200 prior to determining the one orthe plurality of frequency domain features from the acoustic signal at300, normalizing the one or the plurality of frequency domain featuresat 400, prior to identifying the one or more fluid flow locations at 500and/or identifying the at least one of the gas phase flow, an aqueousphase flow, and/or a hydrocarbon liquid phase flow at one or more fluidflow locations, including in some embodiments inflow locations, usingthe plurality of frequency domain features at 600.

As further depicted in the embodiment of FIG. 1, identifying the atleast one of the gas phase flow, the aqueous phase flow, or thehydrocarbon liquid phase flow at the one or more fluid flow locations(e.g., along a tubular, inflow locations, etc.) using the plurality offrequency domain features at 600 can comprise providing the plurality offrequency domain features to a fluid flow model as indicated at 600′,where the model is described in more detail herein. A method ofidentifying fluid inflow according to this disclosure can furthercomprise, at 650, determining a confidence level for the identifying ofthe at least one of the gas phase flow, the aqueous phase flow, or thehydrocarbon liquid phase flow at the one or more fluid flow locationsusing the plurality of frequency domain features at 600 and/ordetermining a relative amounts of the gas phase flow, the aqueous phaseflow, and the hydrocarbon phase flow at 700 prior to determining at 800a remediation procedure based on the relative amounts of the gas phaseflow, the aqueous phase flow, and the hydrocarbon phase flow determinedat 700 and/or the confidence level determined at 650. Each of theaforementioned steps of method I will be described in more detailhereinbelow.

A method of identifying fluid inflow and/or fluid flow according to someembodiments of this disclosure comprises obtaining an acoustic signal at100. Such an acoustic signal can be obtained via any methods known tothose of skill in the art. An exemplary system and method for obtainingthe acoustic signal will now be described with reference to FIG. 2,which is a schematic, cross-sectional illustration of a downholewellbore operating environment 101 according to an embodiment of thisdisclosure. As will be described in more detail below, embodiments ofcompletion assemblies comprising a distributed acoustic sensor (DAS)system as described herein can be positioned in environment 101.

As shown in FIG. 2, exemplary environment 101 includes a wellbore 114traversing a subterranean formation 102, casing 112 lining at least aportion of wellbore 114, and a tubular 120 extending through wellbore114 and casing 112. A plurality of completion assemblies such as spacedscreen elements or assemblies 118 can be provided along tubular 120. Inaddition, a plurality of spaced zonal isolation device 117 and gravelpacks 122 may be provided between tubular 120 and the sidewall ofwellbore 114. In some embodiments, the operating environment 101includes a workover and/or drilling rig positioned at the surface andextending over the wellbore 114. While shown with an exemplarycompletion configuration in FIG. 2, other equipment may be present inplace of or in addition to the equipment illustrated in FIG. 2.

In general, the wellbore 114 can be drilled into the subterraneanformation 102 using any suitable drilling technique. The wellbore 114can extend substantially vertically from the earth's surface over avertical wellbore portion, deviate from vertical relative to the earth'ssurface over a deviated wellbore portion, and/or transition to ahorizontal wellbore portion. In general, all or portions of a wellboremay be vertical, deviated at any suitable angle, horizontal, and/orcurved. In addition, the wellbore 114 can be a new wellbore, an existingwellbore, a straight wellbore, an extended reach wellbore, a sidetrackedwellbore, a multi-lateral wellbore, and other types of wellbores fordrilling and completing one or more production zones. As illustrated,the wellbore 114 includes a substantially vertical producing section150, which is an open hole completion (i.e., casing 112 does not extendthrough producing section 150). Although section 150 is illustrated as avertical and open hole portion of wellbore 114 in FIG. 1, embodimentsdisclosed herein can be employed in sections of wellbores having anyorientation, and in open or cased sections of wellbores. The casing 112extends into the wellbore 114 from the surface and can be cementedwithin the wellbore 114 with cement 111.

The tubular 120 can be lowered into the wellbore 114 for performing anoperation such as drilling, completion, intervention, workover,treatment, and/or production processes. In the embodiment shown in FIG.2, the tubular 120 is a completion assembly string including adistributed acoustic sensor (DAS) sensor coupled thereto. However, ingeneral, embodiments of the tubular 120 can function as a different typeof structure in a wellbore including, without limitation, as a drillstring, casing, liner, jointed tubing, and/or coiled tubing. Further,the tubular 120 may operate in any portion of the wellbore 114 (e.g.,vertical, deviated, horizontal, and/or curved section of wellbore 114).Embodiments of DAS systems described herein can be coupled to theexterior of the tubular 120, or in some embodiments, disposed within aninterior of the tubular 120, as shown in FIGS. 3A and 3B, respectively.When the DAS fiber is coupled to the exterior of the tubular 120, asdepicted in the embodiment of FIG. 3B, the DAS fiber can be positionedwithin a control line, control channel, or recess in the tubular 120. Insome embodiments an outer shroud contains the tubular 120 and protectsthe system during installation. A control line or channel can be formedin the shroud and the DAS fiber can be placed in the control line orchannel.

The tubular 120 can extend from the surface to the producing zones andgenerally provides a conduit for fluids to travel from the formation 102to the surface. A completion assembly including the tubular 120 caninclude a variety of other equipment or downhole tools to facilitate theproduction of the formation fluids from the production zones. Forexample, zonal isolation devices 117 can be used to isolate the variouszones within the wellbore 114. In this embodiment, each zonal isolationdevice 117 can be a packer (e.g., production packer, gravel pack packer,frac-pac packer, etc.). The zonal isolation devices 117 can bepositioned between the screen assemblies 118, for example, to isolatedifferent gravel pack zones or intervals along the wellbore 114 fromeach other. In general, the space between each pair of adjacent zonalisolation devices 117 defines a production interval.

The screen assemblies 118 provide sand control capability. Inparticular, the sand control screen elements 118, or other filter mediaassociated with wellbore tubular 120, can be designed to allow fluids toflow therethrough but restrict and/or prevent particulate matter ofsufficient size from flowing therethrough. The screen assemblies 118 canbe of the type known as “wire-wrapped”, which are made up of a wireclosely wrapped helically about a wellbore tubular, with a spacingbetween the wire wraps being chosen to allow fluid flow through thefilter media while keeping particulates that are greater than a selectedsize from passing between the wire wraps. Other types of filter mediacan also be provided along the tubular 120 and can include any type ofstructures commonly used in gravel pack well completions, which permitthe flow of fluids through the filter or screen while restricting and/orblocking the flow of particulates (e.g. other commercially-availablescreens, slotted or perforated liners or pipes; sintered-metal screens;sintered-sized, mesh screens; screened pipes; prepacked screens and/orliners; or combinations thereof). A protective outer shroud having aplurality of perforations therethrough may be positioned around theexterior of any such filter medium.

The gravel packs 122 are formed in the annulus 119 between the screenelements 118 (or tubular 120) and the sidewall of the wellbore 114 in anopen hole completion. In general, the gravel packs 122 compriserelatively coarse granular material placed in the annulus to form arough screen against the ingress of sand into the wellbore while alsosupporting the wellbore wall. The gravel pack 122 is optional and maynot be present in all completions.

The fluid flowing into the tubular 120 may comprise more than one fluidcomponent. Typical components include natural gas, oil (e.g.,hydrocarbon liquids), water, steam, carbon dioxide, and/or variousmultiphase mixed flows. The fluid flow can further be time varying suchas including slugging, bubbling, or time altering flow rates ofdifferent phases. The relative proportions of these components can varyover time based on conditions within the formation 102 and the wellbore114. Likewise, the composition of the fluid flowing into the tubular 120sections throughout the length of the entire production string can varysignificantly from section to section at any given time.

Fluid can be produced into the wellbore 114 and into the completionassembly string. As the fluid enters the wellbore 114, it may createacoustic sounds that can be detected using an acoustic sensor such as aDAS system. Accordingly, the flow of the various fluids into thewellbore 114 and/or through the wellbore 114 can create vibrations oracoustic sounds that can be detected using sensors to detect thevibrations or acoustic sounds. For example, the vibrations can bedetected using a DAS system, though other point types vibration oracoustic sensors can be used alone or in combination with the DASsystem. Each type of event such as the different fluid flows and fluidflow locations can produce an acoustic signature with unique frequencydomain features.

In FIG. 2, the DAS comprises an optical fiber 162 based acoustic sensingsystem that uses the optical backscatter component of light injectedinto the optical fiber for detecting acoustic perturbations (e.g.,dynamic strain) along the length of the fiber 162. The light can begenerated by a light generator or source 166 such as a laser, which cangenerate light pulses. The optical fiber 162 acts as the sensor elementwith no addition transducers in the optical path, and measurements canbe taken along the length of the entire optical fiber 162. Themeasurements can then be detected by an optical receiver such as sensor164 and selectively filtered to obtain measurements from a given depthpoint or range, thereby providing for a distributed measurement that hasselective data for a plurality of zones along the optical fiber 162 atany given time. In this manner, the optical fiber 162 effectivelyfunctions as a distributed array of microphones spread over the entirelength of the optical fiber 162, which typically spans at least theproduction zone 150 of the wellbore 114, to detect downhole acoustics.

The light backscattered up the optical fiber 162 as a result of theoptical backscatter can travel back to the source, where the signal canbe collected by a sensor 164 and processed (e.g., using a processor168). In general, the time the light takes to return to the collectionpoint is proportional to the distance traveled along the optical fiber162. The resulting backscattered light arising along the length of theoptical fiber 162 can be used to characterize the environment around theoptical fiber 162. The use of a controlled light source 166 (e.g.,having a controlled spectral width and frequency) may allow thebackscatter to be collected and any disturbances along the length of theoptical fiber 162 to be analyzed. In general, any acoustic or dynamicstrain disturbances along the length of the optical fiber 162 can resultin a change in the properties of the backscattered light, allowing for adistributed measurement of both the acoustic magnitude (e.g.,amplitude), frequency and, in some cases, of the relative phase of thedisturbance.

An acquisition device 160 can be coupled to one end of the optical fiber162. As discussed herein, the light source 166 can generate the light(e.g., one or more light pulses), and the sensor 164 can collect andanalyze the backscattered light returning up the optical fiber 162. Insome contexts, the acquisition device 160 including the light source 166and the sensor 164 can be referred to as an interrogator. In addition tothe light source 166 and the sensor 164, the acquisition device 160generally comprises a processor 168 in signal communication with thesensor 164 to perform various analysis steps described in more detailherein. While shown as being within the acquisition device 160, theprocessor can also be located outside of the acquisition device 160including being located remotely from the acquisition device 160. Thesensor 164 can be used to obtain data at various rates and may obtaindata at a sufficient rate to detect the acoustic signals of interestwith sufficient bandwidth. In an embodiment, depth resolution ranges ina range of from about 1 meter to about 10 meters, or less than or equalto about 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 meter can be achieved.Depending on the resolution needs, larger averages or ranges can be usedfor computing purposes. When a high depth resolution is not needed, asystem having a wider resolution (e.g., which may be less expensive) canalso be used in some embodiments.

While the system 101 described herein can be used with a DAS system toacquire an acoustic signal for a location or depth range in the wellbore114, in general, any suitable acoustic signal acquisition system can beused with the method steps disclosed herein. For example, variousmicrophones or other sensors can be used to provide an acoustic signalat a given location based on the acoustic signal processing describedherein. A benefit of the use of the DAS system is that an acousticsignal can be obtained across a plurality of locations and/or across acontinuous length of the wellbore 114 rather than at discrete locations.

Specific spectral signatures can be determined for each event byconsidering one or more frequency domain features of the acoustic signalobtained from the wellbore. The resulting spectral signatures can thenbe used along with processed acoustic signal data to determine if anevent is occurring at a depth range of interest. The events can includevarious fluid flows and/or inflows as described herein. The spectralsignatures can be determined by considering the different types of flowoccurring within a wellbore and characterizing the frequency domainfeatures for each type of flow. In some embodiments, variouscombinations and/or transformations of the frequency domain features canbe used to characterize each type of flow.

FIG. 4 schematically illustrates an exemplary view of an embodiment of awellbore tubular 120 with fluid inflow including a gas phase (e.g., asdepicted as gas bubbles 202) with or without a liquid phase, and shownin the cross-sectional illustrations in FIGS. 3A and 3B, fluid (e.g.,gas, hydrocarbon liquid, water). The gas phase depicted as 202 can flowfrom the formation 102 into the wellbore 114 and then into the tubular120. As the fluid 202 flows into the tubular 120, various acousticsignals can be generated, and as the fluid 202 flows within the tubular120, additional acoustic signals, which can be the same or differentthan the inflow signals, can also be generated. The acoustic signals canthen be detected by the DAS fiber and recorded using the DAS system.Without being limited by this or any particular theory, the spectralcharacteristics of the sounds generated by each type of fluid flowand/or inflow can depend on the effective mass and flow rate of eachfluid. In some embodiments, the acoustic signals obtained at 100 caninclude frequencies in the range of about 5 Hz to about 10 kHz,frequencies in the range of about 5 Hz to about 5 kHz or about 50 Hz toabout 5 kHz, or frequencies in the range of about 500 Hz to about 5 kHz.Any frequency ranges between the lower frequency values (e.g., 5 Hz, 50Hz, 500 Hz, etc.) and the upper frequency values (e.g., 10 kHz, 7 kHz, 5kHz, etc.) can be used to define the frequency range for a broadbandacoustic signal.

Taking gas flow and/or inflow as an example, the proximity to theoptical fiber 162 can result in a high likelihood that any acousticsignals generated would be detected by the optical fiber 162. The flowof a gas into the wellbore would likely result in a turbulent flow overa broad frequency range. For example, the gas inflow acoustic signalscan be between about 0 Hz and about 1000 Hz, or alternatively betweenabout 0 Hz and about 500 Hz. An increased power intensity may occurbetween about 300 Hz and about 500 Hz from increased turbulence in thegas flow. An example of the acoustic signal resulting from the influx ofgas into the wellbore is shown in FIG. 5, which illustrates frequencyfiltered acoustic intensity in depth versus time graphs for fivefrequency bins. As illustrated, the five frequency bins represent 5 Hzto 50 Hz, 50 Hz to 100 Hz, 100 Hz to 500 Hz, 500 Hz to 2000 Hz, and 2000Hz to 5000 Hz. The acoustic intensity can be seen in the first threebins with frequency ranges up to about 500 Hz, with a nearlyundetectable acoustic intensity in the frequency range above 500 Hz.This demonstrates that at least a portion of the frequency domainfeatures may not be present above 500 Hz, which can help to define thesignature of the influx of gas. This type of response demonstrates thateach event can be expected to produce an acoustic response havingpotentially unique feature sets that can be used to help define asignature for the event. While described in terms of frequency ranges orbins, other features and transformations of such features can be used tohelp define the gas flow and/or inflow signatures, which can be usedwith a multivariate model for determining if gas flow and/or inflow ispresent.

Similar frequency features can be expected for other fluid inflows aswell as fluid flows along a tubular within the wellbore. The resultingacoustic signal can be processed to determine a plurality of frequencydomain features. The signatures for each type of fluid flow can then bebased on a plurality of frequency domain features. This can includetransforming one or more of the frequency domain features to serve as anelement of a specific fluid flow signature, as described in more detailherein.

Referring again to FIG. 2, the processor 168 within the acquisitiondevice 160 can be configured to perform various data processing todetect the presence of fluid inflow along the length of the wellbore114. The acquisition device 160 can comprise a memory 170 configured tostore an application or program to perform the data analysis. Whileshown as being contained within the acquisition device 160, the memory170 can comprise one or more memories, any of which can be external tothe acquisition device 160. In an embodiment, the processor 168 canexecute the program, which can configure the processor 168 to filter theacoustic data set spatially, determine one or more frequency domainfeatures of the acoustic signal, and determine whether or not fluidinflow is occurring at the selected location based on the analysisdescribed hereinbelow, and whether any fluid inflow comprises waterinflow, hydrocarbon liquid inflow, and gas inflow. The analysis can berepeated across various locations along the length of the wellbore 114to determine the locations of fluid inflow and/or the type of fluid(e.g., gas, water, hydrocarbon liquid) inflowing along the length of thewellbore 114.

When the acoustic sensor comprises a DAS system, the optical fiber 162can return raw optical data in real time or near real time to theacquisition unit 160. In an embodiment, the raw data can be stored inthe memory 170 for various subsequent uses. The sensor 164 can beconfigured to convert the raw optical data into an acoustic data set.

As shown schematically in FIG. 6, an embodiment of a system 401 fordetecting fluid and/or fluid phase of an inflow can comprise a dataextraction unit 402, a processing unit 404, and/or an output orvisualization unit 406. The data extraction unit 402 can obtain theoptical data and perform the initial pre-processing steps to obtain theinitial acoustic information from the signal returned from the wellbore.Various analyses can be performed including frequency band extraction,frequency analysis and/or transformation, intensity and/or energycalculations, and/or determination of one or more properties of theacoustic data. Following the data extraction unit 402, the resultingsignals can be sent to a processing unit 404. Within the processingunit, the acoustic data can be analyzed, for example, by calculating oneor more frequency domain features and utilizing a model or modelsobtained from a machine learning approach (e.g., a supervised learningapproach, etc.) on the one or more frequency domain features asdescribed further hereinbelow to determine if fluid flow and/or inflowis present, and, if present, determining if the fluid flow and/or inflowcomprises water flow and/or inflow, hydrocarbon liquid flow and/orinflow, and/or gas flow and/or inflow.

One or more models can also be used to determine the presence of variousfluid flow regimes within a conduit within the wellbore. In someembodiments, the machine learning approach comprises a logisticregression model. In some such embodiments, a single frequency domainfeature (e.g., spectral flatness, RMS bin values, etc.) can be used todetermine if fluid inflow is present at each location of interest. Insome embodiments, the supervised learning approach can be used todetermine a model of the various flow regimes such as a first polynomialhaving the plurality of frequency domain features as inputs to determinewhen gas phase inflow is present, a second polynomial having theplurality of frequency domain features as inputs to determine whenaqueous phase inflow is present, and a third polynomial having theplurality of frequency domain features as inputs to determine whenhydrocarbon liquid phase inflow is present. Once the processing unit 404uses the model obtained from the machine learning approach to determinethe presence or lack of fluid inflow (e.g., gas inflow, water inflow,hydrocarbon liquid inflow, etc.) and the composition thereof (e.g., gas,hydrocarbon liquid, water), the resulting analysis information can thenbe sent from the processing unit 404 to the output/visualization unit406 where various information such a visualization of the location ofthe inflow and/or information providing quantification information(e.g., a relative amount of gas inflow, water inflow, hydrocarbon liquidinflow, and the like) can be visualized in a number of ways. In anembodiment, the resulting event information can be visualized on a wellschematic, on a time log, or any other number of displays to aid inunderstanding where the inflow is occurring, and in some embodiments, todisplay a relative amount of the various components of the inflowingfluid occurring at one or more locations along the length of thewellbore. While illustrated in FIG. 6 as separate units, any two or moreof the units shown in FIG. 6 can be incorporated into a single unit. Forexample, a single unit can be present at the wellsite to provideanalysis, output, and optionally, visualization of the resultinginformation.

As noted above, a method of identifying fluid flow according to thisdisclosure can comprise preprocessing the acoustic signal. The acousticsignal can be generated within the wellbore as described herein.Depending on the type of DAS system employed, the optical data may ormay not be phase coherent and may be pre-processed to improve the signalquality (e.g., denoised for opto-electronic noisenormalization/de-trending single point-reflection noise removal throughthe use of median filtering techniques or even through the use ofspatial moving average computations with averaging windows set to thespatial resolution of the acquisition unit, etc.). The raw optical datafrom the acoustic sensor can be received and generated by the sensor toproduce the acoustic signal. The data rate generated by various acousticsensors such as the DAS system can be large. For example, the DAS systemmay generate data on the order of 0.5 to about 2 terabytes per hour.This raw data can optionally be stored in a memory.

The raw data can then be optionally pre-processed in step 200. A numberof specific processing steps can be performed to determine the presenceof fluid inflow and/or the composition of inflowing fluid. In anembodiment, the noise detrended “acoustic variant” data can be subjectedto an optional spatial filtering step following the other pre-processingsteps, if present. A spatial sample point filter can be applied thatuses a filter to obtain a portion of the acoustic signal correspondingto a desired depth in the wellbore. Since the time the light pulse sentinto the optical fiber returns as backscattered light can correspond tothe travel distance, and therefore depth in the wellbore, the acousticdata can be processed to obtain a sample indicative of the desired depthor depth range. This may allow a specific location within the wellboreto be isolated for further analysis. The pre-processing step may alsoinclude removal of spurious back reflection type noises at specificdepths through spatial median filtering or spatial averaging techniques.This is an optional step and helps focus primarily on an interval ofinterest in the wellbore. For example, the spatial filtering step can beused to focus on a producing interval where there is maximum likelihoodof fluid inflow, for example. The resulting data set produced throughthe conversion of the raw optical data can be referred to as theacoustic sample data.

Filtering can provide several advantages. When the acoustic data set isspatially filtered, the resulting data, for example the acoustic sampledata, used for the next step of the analysis can be indicative of anacoustic sample over a defined depth (e.g., the entire length of theoptical fiber, some portion thereof, or a point source in the wellbore114). In some embodiments, the acoustic data set can comprise aplurality of acoustic samples resulting from the spatial filter toprovide data over a number of depth ranges. In some embodiments, theacoustic sample may contain acoustic data over a depth range sufficientto capture multiple points of interest. In some embodiments, theacoustic sample data contains information over the entire frequencyrange at the depth represented by the sample. This is to say that thevarious filtering steps, including the spatial filtering, do not removethe frequency information from the acoustic sample data.

The processor 168 can be further configured to transform the filtereddata from the time domain into the frequency domain using a transform.For example, Discrete Fourier transformations (DFT) or a short timeFourier transform (STFT) of the acoustic variant time domain datameasured at each depth section along the fiber or a section thereof maybe performed to provide the data from which the plurality of frequencydomain features can be determined. Spectral feature extraction throughtime and space can be used to determine the spectral conformance anddetermine if an acoustic signature (e.g., a fluid inflow signature, agas phase inflow signature, a water phase inflow signature, ahydrocarbon liquid phase inflow signature, etc.) is present in theacoustic sample. Within this process, various frequency domain featurescan be calculated for the acoustic sample data.

Preprocessing at 200 can optionally include a noise normalizationroutine to improve the signal quality. This step can vary depending onthe type of acquisition device used as well as the configuration of thelight source, the sensor, and the other processing routines. The orderof the aforementioned preprocessing steps can be varied, and any orderof the steps can be used.

Preprocessing at 200 can further comprise calibrating the acousticsignal. Calibrating the acoustic signal can comprise removing abackground signal from the acoustic signal, and/or correcting theacoustic signal for signal variations in the measured data. In someembodiments, calibrating the acoustic signal comprises identifying oneor more anomalies within the acoustic signal and removing one or moreportions of the acoustic signal outside the one or more anomalies. Asnoted hereinabove, a method of this disclosure comprises determining oneor more frequency domain features or indicators at step 300. The use offrequency domain features to identify inflow locations and inflowdiscrimination can provide a number of advantages. First, the use offrequency domain features results in significant data reduction relativeto the raw DAS data stream. Thus, a number of frequency domain featurescan be calculated and used to allow for event identification while theremaining data can be discarded or otherwise stored, and the remaininganalysis can be performed using the frequency domain features. Even whenthe raw DAS data is stored, the remaining processing power issignificantly reduced through the use of the frequency domain featuresrather than the raw acoustic data itself. Further, the use of thefrequency domain features can, with the appropriate selection of one ormore of the frequency domain features, provide a concise, quantitativemeasure of the spectral character or acoustic signature of specificsounds pertinent to downhole fluid surveillance and other applications.

While a number of frequency domain features can be determined for theacoustic sample data, not every frequency domain feature may be used inthe identifying fluid flow characteristics, the locations of fluidinflow, or identifying at least one of a gas phase inflow, an aqueousphase inflow, or a hydrocarbon liquid phase inflow. The frequency domainfeatures represent specific properties or characteristics of theacoustic signals. There are a number of factors that can affect thefrequency domain feature selection for each fluid inflow event. Forexample, a chosen descriptor should remain relatively unaffected by theinterfering influences from the environment such as interfering noisefrom the electronics/optics, concurrent acoustic sounds, distortions inthe transmission channel, and the like. In general,electronic/instrumentation noise is present in the acoustic signalscaptured on the DAS or any other electronic gauge, and it is usually anunwanted component that interferes with the signal. Thermal noise isintroduced during capturing and processing of signals by analoguedevices that form a part of the instrumentation (e.g., electronicamplifiers and other analog circuitry). This is primarily due to thermalmotion of charge carriers. In digital systems additional noise may beintroduced through sampling and quantization. The frequency domainfeatures should have values that are significant for a given even in thepresence of noise.

As a further consideration in selecting the frequency domain feature(s)for a fluid inflow event, the dimensionality of the frequency domainfeature should be compact. A compact representation is desired todecrease the computational complexity of subsequent calculations. Thefrequency domain feature should also have discriminant power. Forexample, for different types of audio signals, the selected set ofdescriptors should provide altogether different values. A measure forthe discriminant power of a feature is the variance of the resultingfeature vectors for a set of relevant input signals. Given differentclasses of similar signals, a discriminatory descriptor should have lowvariance inside each class and high variance over different classes. Thefrequency domain feature should also be able to completely cover therange of values of the property it describes.

In some embodiments, combinations of frequency domain features can beused. This can include a signature having multiple frequency domainfeatures as indicators. In some embodiments, a plurality of frequencydomain features can be transformed to create values that can be used todefine various event signatures. This can include mathematicaltransformations including ratios, equations, rates of change, transforms(e.g., wavelets, Fourier transforms, other wave form transforms, etc.),other features derived from the feature set, and/or the like as well asthe use of various equations that can define lines, surfaces, volumes,or multi-variable envelopes. The transformation can use othermeasurements or values outside of the frequency domain features as partof the transformation. For example, time domain features, other acousticfeatures, and non-acoustic measurements can also be used. In this typeof analysis, time can also be considered as a factor in addition to thefrequency domain features themselves. As an example, a plurality offrequency domain features can be used to define a surface (e.g., aplane, a three-dimensional surface, etc.) in a multivariable space, andthe measured frequency domain features can then be used to determine ifthe specific readings from an acoustic sample fall above or below thesurface. The positioning of the readings relative to the surface canthen be used to determine if the event if present or not at thatlocation in that detected acoustic sample.

As an example, the chosen set of frequency domain features should beable to uniquely identify the event signatures with a reasonable degreeof certainty of each of the acoustic signals pertaining to a selecteddownhole surveillance application or fluid inflow event as describedherein. Such frequency domain features can include, but are not limitedto, the spectral centroid, the spectral spread, the spectral roll-off,the spectral skewness, the root mean square (RMS) band energy (or thenormalized subband energies/band energy ratios), a loudness or total RMSenergy, a spectral flatness, a spectral slope, a spectral kurtosis, aspectral flux, a spectral autocorrelation function, or a normalizedvariant thereof.

The spectral centroid denotes the “brightness” of the sound captured bythe optical fiber 162 and indicates the center of gravity of thefrequency spectrum in the acoustic sample. The spectral centroid can becalculated as the weighted mean of the frequencies present in thesignal, where the magnitudes of the frequencies present can be used astheir weights in some embodiments. The value of the spectral centroid,C_(i), of the i^(th) frame of the acoustic signal captured at a spatiallocation on the fiber, may be written as:

$\begin{matrix}{{C_{i} = \frac{\sum_{k = 1}^{N}{{f(k)}{X_{i}(k)}}}{\sum_{k = 1}^{N}{X_{i}(k)}}},} & \left( {{Eq}.\mspace{14mu} 1} \right)\end{matrix}$

where X_(i)(k), is the magnitude of the short time Fourier transform ofthe i^(th) frame where ‘k’ denotes the frequency coefficient or binindex, N denotes the total number of bins and f(k) denotes the centrefrequency of the bin. The computed spectral centroid may be scaled tovalue between 0 and 1. Higher spectral centroids typically indicate thepresence of higher frequency acoustics and help provide an immediateindication of the presence of high frequency noise.

The spectral spread can also be determined for the acoustic sample. Thespectral spread is a measure of the shape of the spectrum and helpsmeasure how the spectrum is distributed around the spectral centroid. Inorder to compute the spectral spread, S_(i), one has to take thedeviation of the spectrum from the computed centroid as per thefollowing equation (all other terms defined above):

$\begin{matrix}{S_{i} = {\sqrt{\frac{\sum_{k = 1}^{N}{\left( {{f(k)} - C_{i}} \right)^{2}{X_{i}(k)}}}{\sum_{k = 1}^{N}{X_{i}(k)}}}.}} & \left( {{Eq}.\mspace{14mu} 2} \right)\end{matrix}$

Lower values of the spectral spread correspond to signals whose spectraare tightly concentrated around the spectral centroid. Higher valuesrepresent a wider spread of the spectral magnitudes and provide anindication of the presence of a broad band spectral response.

The spectral roll-off is a measure of the bandwidth of the audio signal.The Spectral roll-off of the i^(th) frame, is defined as the frequencybin ‘y’ below which the accumulated magnitudes of the short-time Fouriertransform reach a certain percentage value (usually between 85%-95%) ofthe overall sum of magnitudes of the spectrum.

$\begin{matrix}{{{\sum_{k = 1}^{y}{{X_{i}(k)}}} = {\frac{c}{100}{\sum_{k = 1}^{N}{{X_{i}(k)}}}}},} & \left( {{Eq}.\mspace{14mu} 3} \right)\end{matrix}$

where c=85 or 95. The result of the spectral roll-off calculation is abin index and enables distinguishing acoustic events based on dominantenergy contributions in the frequency domain. (e.g., between gas influxand liquid flow, etc.)

The spectral skewness measures the symmetry of the distribution of thespectral magnitude values around their arithmetic mean.

The RMS band energy provides a measure of the signal energy withindefined frequency bins that may then be used for signal amplitudepopulation. The selection of the bandwidths can be based on thecharacteristics of the captured acoustic signal. In some embodiments, asub-band energy ratio representing the ratio of the upper frequency inthe selected band to the lower frequency in the selected band can rangebetween about 1.5:1 to about 3:1. In some embodiments, the subbandenergy ratio can range from about 2.5:1 to about 1.8:1, or alternativelybe about 2:1. In some embodiment, selected frequency ranges for a signalwith a 5,000 Hz Nyquist acquisition bandwidth can include: a first binwith a frequency range between 0 Hz and 20 Hz, a second bin with afrequency range between 20 Hz and 40 Hz, a third bin with a frequencyrange between 40 Hz and 80 Hz, a fourth bin with a frequency rangebetween 80 Hz and 160 Hz, a fifth bin with a frequency range between 160Hz and 320 Hz, a sixth bin with a frequency range between 320 Hz and 640Hz, a seventh bin with a frequency range between 640 Hz and 1280 Hz, aneighth bin with a frequency range between 1280 Hz and 2500 Hz, and aninth bin with a frequency range between 2500 Hz and 5000 Hz. In someembodiments, a low frequency threshold can be used to help to reducenoise in the signal. For example, a lower frequency threshold between 0and 5 Hz, between 0 and 10 Hz, or between 0 and 15 Hz can be used, whichcan result in the first bin including a frequency range between 5 Hz and20 Hz, between 10 Hz and 20 Hz, or between 15 Hz and 20 Hz depending onthe lower frequency threshold used. In some embodiments, a ninth bin canbe defined as cover the entire frequency range covered by the otherbins. For example, a ninth bin can have a frequency range from 0 Hz to5,000 Hz (or between 5 Hz and 5,000 Hz, 10 Hz and 5,000 Hz, or 15 Hz and5,000 Hz, depending on whether or not a lower threshold is used and thechoice of that threshold). The bin covering the entire frequency rangecan be used, in some embodiments, to normalize the measurements withineach individual bin. While certain frequency ranges for each bin arelisted herein, they are used as examples only, and other values in thesame or a different number of frequency range bins can also be used. Insome embodiments, the RMS band energies may also be expressed as aratiometric measure by computing the ratio of the RMS signal energywithin the defined frequency bins relative to the total RMS energyacross the acquisition (Nyquist) bandwidth. This may help to reduce orremove the dependencies on the noise and any momentary variations in thebroadband sound.

The total RMS energy of the acoustic waveform calculated in the timedomain can indicate the loudness of the acoustic signal. In someembodiments, the total RMS energy can also be extracted from thetemporal domain after filtering the signal for noise.

The spectral flatness is a measure of the noisiness/tonality of anacoustic spectrum. It can be computed by the ratio of the geometric meanto the arithmetic mean of the energy spectrum value and may be used asan alternative approach to detect broadbanded signals. For tonalsignals, the spectral flatness can be close to 0 and for broader bandsignals it can be closer to 1.

The spectral slope provides a basic approximation of the spectrum shapeby a linearly regressed line. The spectral slope represents the decreaseof the spectral amplitudes from low to high frequencies (e.g., aspectral tilt). The slope, the y-intersection, and the max and mediaregression error may be used as features.

The spectral kurtosis provides a measure of the flatness of adistribution around the mean value.

The spectral flux is a measure of instantaneous changes in the magnitudeof a spectrum. It provides a measure of the frame-to-frame squareddifference of the spectral magnitude vector summed across allfrequencies or a selected portion of the spectrum. Signals with slowlyvarying (or nearly constant) spectral properties (e.g., noise) have alow spectral flux, while signals with abrupt spectral changes have ahigh spectral flux. The spectral flux can allow for a direct measure ofthe local spectral rate of change and consequently serves as an eventdetection scheme that could be used to pick up the onset of acousticevents that may then be further analyzed using the feature set above toidentify and uniquely classify the acoustic signal.

The spectral autocorrelation function provides a method in which thesignal is shifted, and for each signal shift (lag) the correlation orthe resemblance of the shifted signal with the original one is computed.This enables computation of the fundamental period by choosing the lag,for which the signal best resembles itself, for example, where theautocorrelation is maximized. This can be useful in exploratorysignature analysis/even for anomaly detection for well integritymonitoring across specific depths where well barrier elements to bemonitored are positioned.

Any of these frequency domain features, or any combination of thesefrequency domain features (including transformations of any of thefrequency domain features and combinations thereof), can be used toidentify the location of fluid inflow or the fluid inflow discriminationas described hereinbelow. In an embodiment, a selected set ofcharacteristics can be used to identify the presence or absence for eachfluid inflow event, and/or all of the frequency domain features that arecalculated can be used as a group in characterizing the presence orabsence of a fluid inflow event. The specific values for the frequencydomain features that are calculated can vary depending on the specificattributes of the acoustic signal acquisition system, such that theabsolute value of each frequency domain feature can change betweensystems. In some embodiments, the frequency domain features can becalculated for each event based on the system being used to capture theacoustic signal and/or the differences between systems can be taken intoaccount in determining the frequency domain feature values for eachfluid inflow event between or among the systems used to determine thevalues and the systems used to capture the acoustic signal beingevaluated.

One or a plurality of frequency domain features can be used tocharacterize each type of event (e.g., fluid inflow, water inflow, gasinflow, hydrocarbon liquid inflow). In an embodiment, one, at least two,alternatively at least three, alternatively at least four, alternativelyat least five, alternatively at least six, alternatively at least seven,or alternatively at least eight different frequency domain features canbe used to characterize each type of event (e.g., fluid inflow, waterinflow, gas inflow, hydrocarbon liquid inflow). The frequency domainfeatures can be combined or transformed in order to define the eventsignatures for one or more events. While exemplary numerical ranges areprovided herein, the actual numerical results may vary depending on thedata acquisition system and/or the values can be normalized or otherwiseprocessed to provide different results.

As noted above, in order to obtain the frequency domain features, theacoustic sample data can be converted to the frequency domain atpreprocessing step 200. In an embodiment, the raw optical data maycontain or represent acoustic data in the time domain. Thus, in someembodiments, preprocessing at 200 comprises obtaining a frequency domainrepresentation of the data using a Fourier Transform. Various algorithmscan be used as known in the art. In some embodiments, a Short TimeFourier Transform technique or a Discrete Time Fourier transform can beused. The resulting data sample may then be represented by a range offrequencies relative to their power levels at which they are present.The raw optical data can be transformed into the frequency domain priorto or after the application of the spatial filter. In general, theacoustic sample will be in the frequency domain in order to determinethe frequency domain feature(s). In some embodiments, the processor 168can be configured to perform the conversion of the raw acoustic dataand/or the acoustic sample data from the time domain into the frequencydomain. In the process of converting the signal to the frequency domain,the power across all frequencies within the acoustic sample can beanalyzed. The use of the processor 168 to perform the transformation mayprovide the frequency domain data in real time or near real time.

The processor 168 can then be used to analyze the acoustic sample datain the frequency domain to obtain one or more of the frequency domainfeatures and provide an output with the determined frequency domainfeatures for further processing. In some embodiments, the output of thefrequency domain features can include features that are not used todetermine the presence of fluid inflow, water phase inflow, gas inflow,hydrocarbon liquid inflow.

The output of the processor with the frequency domain features for theacoustic sample data can then be used to determine the presence of oneor more fluid flow and/or inflow events at one or more locations in thewellbore corresponding to depth intervals over which the acoustic datais acquired or filtered.

A method of identifying fluid inflow can optionally comprise normalizingthe one or the plurality of frequency domain features at 400 prior toidentifying the one or more fluid inflow locations at 500 and/or priorto identifying the at least one of the gas phase inflow, the aqueousphase inflow, or the hydrocarbon liquid phase inflow at 600.

A method of identifying fluid flow and/or inflow according to thisdisclosure can comprise identifying one or more fluid flow and/or inflowlocations at 500. Such fluid inflow locations can be determined as knownto those of skill in the art, for example via PLS data. In someembodiments, the one or more fluid inflow locations are determined asdescribed hereinbelow. In such embodiments, identifying one or morefluid flow and/or inflow locations can comprise identifying the one ormore fluid flow and/or inflow locations using one or more of thefrequency domain features to identify acoustic signals corresponding tothe flow and/or inflow, and correlating the depths of those signals withlocations within the wellbore. The one or more frequency domain featurescan comprise at least two different frequency domain features. In someembodiments, the one or more frequency domain features utilized todetermine the one or more fluid inflow locations comprises at least oneof a spectral centroid, a spectral spread, a spectral roll-off, aspectral skewness, an RMS band energy, a total RMS energy, a spectralflatness, a spectral slope, a spectral kurtosis, a spectral flux, aspectral autocorrelation function, combinations and/or transformationsthereof, and/or a normalized variant thereof. In some embodiments, theone or more frequency domain features utilized to determine the one ormore fluid inflow locations include a spectral flatness, an RMS bandenergy, a total RMS energy, or a normalized variant of one or more ofthe spectral flatness, the RMS band energy, the total RMS energy, or acombination thereof.

In some embodiments, identifying the one or more fluid inflow locationscomprises: identifying a background fluid flow signature using theacoustic signal; and removing the background fluid flow signature fromthe acoustic signal prior to identifying the one or more fluid inflowlocations. In some embodiments, identifying the one or more fluid inflowlocations comprises identifying one or more anomalies in the acousticsignal using the one or more frequency domain features of the pluralityof frequency domain features; and selecting the depth intervals of theone or more anomalies as the one or more inflow locations.

When a portion of the signal is removed (e.g., a background fluid flowsignature, etc.), the removed portion can also be used as part of theevent analysis. In some embodiments, identifying the one or more fluidinflow locations comprises: identifying a background fluid flowsignature using the acoustic signal; and using the background fluid flowsignature from the acoustic signal to identify as event such as one ormore fluid flow events.

In some embodiments, a method of identifying fluid flow and/or inflowaccording to this disclosure comprises identifying at least one of a gasphase inflow, an aqueous phase inflow, or a hydrocarbon liquid phaseinflow using a plurality of frequency domain features at the identifiedone or more fluid inflow locations at 600. In some embodiments, theplurality of frequency domain features utilized for identifying the atleast one of the gas phase inflow, the aqueous phase inflow, or thehydrocarbon liquid phase inflow at the identified one or more fluidinflow locations comprises at least two of: a spectral centroid, aspectral spread, a spectral roll-off, a spectral skewness, an RMS bandenergy, a total RMS energy, a spectral flatness, a spectral slope, aspectral kurtosis, a spectral flux, a spectral autocorrelation function,or a normalized variant thereof.

In some embodiments, identifying at least one of the gas phase inflow,the aqueous phase inflow, or the hydrocarbon liquid phase inflow usingthe plurality of the frequency domain features at 600 comprises:identifying the at least one of the gas phase inflow, the aqueous phaseinflow, or the hydrocarbon liquid phase inflow using a valuerepresenting a transformation of at least one of the plurality of thefrequency domain features. In some embodiments, identifying the at leastone of the fluid flow, gas phase inflow, the aqueous phase inflow, orthe hydrocarbon liquid phase inflow using the plurality of the frequencydomain features at 600 comprises: identifying the at least one of afluid flow (e.g., a gas phase flow, an aqueous phase flow, and/or ahydrocarbon phase flow), a gas phase inflow, an aqueous phase inflow, ahydrocarbon liquid phase inflow, or any combination thereof using amultivariate model (e.g., one or more polynomial equations, mathematicalformulas, etc.) that defines a relationship between at least two of theplurality of the frequency domain features, including in someembodiments transformations of the frequency domain features. In someembodiments, identifying the at least one of the gas phase inflow, theaqueous phase inflow, or the hydrocarbon liquid phase inflow using theplurality of the frequency domain features at 600 comprises: identifyingthe presence or absence of a gas phase using a first multivariate modelhaving a first at least two of the plurality of frequency domainfeatures as inputs to determine when the gas phase inflow is present,identifying the presence or absence of aqueous phase inflow using asecond multivariate model having a second at least two of the pluralityof frequency domain features as inputs to determine when the aqueousphase inflow is present, and identifying the presence or absence of anaqueous phase inflow using a third polynomial having a third at leasttwo of the plurality of frequency domain features as inputs to determinewhen the hydrocarbon liquid phase inflow is present. The first at leasttwo, the second at least two, and the third at least two of theplurality of frequency domain features can be the same or different. Insome embodiments, identifying at least one of the fluid flow, gas phaseinflow, the aqueous phase inflow, or the hydrocarbon liquid phase inflowusing the plurality of the frequency domain features at 600 comprises:identifying at least one of the gas phase inflow, the aqueous phaseinflow, or the hydrocarbon liquid phase inflow using a ratio between atleast two of the plurality of the frequency domain features.

In some embodiments, identifying at least one of the gas phase inflow,the aqueous phase inflow, or the hydrocarbon liquid phase inflowcomprises providing the plurality of frequency domain features to afluid flow and/or inflow (e.g., a logistic regression) model at 600′ foreach of the gas phase, the aqueous phase, and the hydrocarbon liquidphase; and determining that at least one of the gas phase, the aqueousphase, or the hydrocarbon liquid phase is present based on the fluidflow model. In some embodiments, the fluid flow model can be developedusing and/or include machine learning such as a neural network, aBayesian network, a decision tree, a logistical regression model, or anormalized logistical regression, or other supervised learning models.

In some embodiments, the fluid flow and/or inflow model can use a firstmultivariate model having at least two of the plurality of frequencydomain features as inputs to determine when the gas phase inflow ispresent. The logistic regression model can use a second multivariatemodel having a second at least two of the plurality of frequency domainfeatures as inputs to determine when the aqueous phase inflow ispresent, and the logistic regression model can use a third multivariatemodel having a third at least two of the plurality of frequency domainfeatures as inputs to determine when the hydrocarbon liquid phase inflowis present. The first at least two, the second at least two, and thethird at least two of the frequency domain features can be the same ordifferent.

The use of different models for one or more types of fluid inflow eventscan allow for a more accurate determination of each event. The modelscan differ in a number of ways. For example, the models can havedifferent parameters, different mathematical determinations, bedifferent types of models, and/or use different frequency domainfeatures. In some embodiments, a plurality of models can be used fordifferent fluid inflow events, and at least one of the models can havedifferent parameters. In general, parameters refer to constants orvalues used within the models to determine the output of the model. Inmultivariate models as an example, the parameters can be coefficients ofone or more terms in the equations in the models. In neural networkmodels as an example, the parameters can be the weightings applied toone or more nodes. Other constants, offsets, and coefficients in varioustypes of models can also represent parameters. The use of differentparameters can provide a different output amongst the models when themodels are used to identify different types of fluid inflow events.

The models can also differ in their mathematical determinations. Inmultivariate models, the models can comprise one or more terms that canrepresent linear, non-linear, power, or other functions of the inputvariables (e.g., one or more frequency domain features, etc.). Thefunctions can then change between the models. As another example, aneural network may have different numbers of layers and nodes, therebycreating a different network used with the input variables. Thus, evenwhen the same frequency domain features are used in two more models, theoutputs can vary based on the different functions and/or structures ofthe models.

The models can also be different on the basis of being different typesof models. For example, the plurality of models can use regressionmodels to identify one or more fluid inflow events and neural networksfor different fluid inflow events. Other types of models are alsopossible and can be used to identify different types of inflow events.Similarly, the models can be different by using different inputvariables. The use of different variables can provide different outputsbetween the models. The use of different models can allow for the sameor different training data to be used to produce more accurate resultsfor different types of fluid inflow events. Any of the models describedherein can rely on the use of different models for different types offluid inflow events (e.g., a gas phase inflow, an aqueous phase inflow,a hydrocarbon liquid phase inflow, etc.), as described in more detailherein.

In some embodiments, identifying at least one of the fluid flow, the gasphase inflow, the aqueous phase inflow, or the hydrocarbon liquid phasecomprises utilizing a fluid flow model and using the plurality offrequency domain features at the identified one or more fluid inflowlocations in the first multivariate model; using the plurality offrequency domain features at the identified one or more fluid inflowlocations in the second multivariate model; using the plurality offrequency domain features at the identified one or more fluid inflowlocations in the third multivariate model; comparing the plurality offrequency domain features to an output of the first multivariate model,an output of the second multivariate model, and an output of the thirdmultivariate model; and identifying at least one of the gas phaseinflow, the aqueous phase inflow, or the hydrocarbon liquid phase inflowbased on the comparison of the plurality of frequency domain features tothe output of the first multivariate model, the output of the secondmultivariate model, and the output of the third multivariate model.

In some embodiments, the plurality of frequency domain features utilizedto identify the fluid flow, at least one of the gas phase inflow, theaqueous phase inflow, or the hydrocarbon liquid phase inflow can includea normalized variant of the spectral spread and/or a normalized variantof the spectral centroid, and the fluid inflow (e.g., logisticregression) model can define a relationship between a presence orabsence of the gas phase inflow, the aqueous phase inflow, or thehydrocarbon liquid phase inflow at the location of the acoustic signal.

In addition to the multivariate model(s) used to determine the presenceand identity of the fluid flows and/or inflows, a multivariate model canbe developed to identify fluid flow and the composition of the flowbased on building a predicted data set for the flow using the inflowdata. In this multivariate model, the volume of a hydrocarbon gas phase,a hydrocarbon liquid phase, and an aqueous phase can be predicted usingthe inflow profiling analysis. In order to predict a volume of eachphase, a magnitude of one or more frequency domain features associatedwith each phase can be used to determine a volume of fluid. Theresulting volume data can then be aggregated across each inflow locationto predict the total volume of each fluid phase flowing within theconduit at a given location. This type of multivariate model can bedeveloped using regularized multivariate linear regression. Further,this type of data can be used to predict fluid phase and volume flows ina manner similar to that provided by a PLS disposed in a wellbore. Thedata can then be compared to further refine and/or develop themultivariate model based on a comparison with actual PLS data from thewellbore.

Other multivariate models can also be developed using the processesdescribed herein. In some embodiments, test data can be generated for anexpected event within a wellbore using a flow loop or flow testapparatus as disclosed herein. The desired event or flow can be created,and the test data can be captured. The resulting labeled data sets canbe used to train one or more multivariate models to determine thepresence of the event using one or more frequency domain features.

As an example of an additional multivariate model, sand inflow and/orflow in a fluid phase within a conduit can be modeled. The sand flow canbe modeled in different fluid phases, at different sand amounts, indifferent orientations, and through different types of productionassemblies, pipes, annuli, and the like. The resulting acoustic data canbe used in the model development process as disclosed herein todetermine one or more multivariate models indicative of the presence ofsand in an inflowing fluid in one or more fluid phases and/or in aflowing fluid within the wellbore within one or more fluid phases. Suchmultivariate model may then be used with detected acoustic data todetermine if sand is present in various fluids while allowing fordiscrimination between sand inflow and/or sand flow along the wellbore.

In some embodiments, the model at 600′ can be developed using machinelearning. In order to develop and validate the model, data having knownfluid flows and acoustic signals can be used as the basis for trainingand/or developing the model parameters. This data set can be referred toas a labeled data set (e.g., a data set for which the flow regime and/orinflow location is already known) that can be used for training themodels (e.g., the multivariate model) in some instances. In someembodiments, the known data can be data from a wellbore having flowmeasured by various means. In some embodiments, the data can be obtainedusing a test setup where known quantities of various fluids (e.g., gas,hydrocarbon liquids, aqueous liquids, etc.) can be introduced atcontrolled point to generate controlled fluid flow and/or inflows. Atleast a portion of the data can be used to develop the model, andoptionally, a portion of the data can be used to test the model once itis developed.

FIG. 7 illustrates a flow diagram of a method II of developing a fluididentification or flow model according to some embodiments. The methodcan comprise, at 900, obtaining acoustic data or signals from aplurality of flow and/or inflow tests in which one or more fluids of aplurality of fluids are introduced into a a conduit at predeterminedlocations spanning a length of the conduit, wherein the plurality offluids comprise a hydrocarbon gas, a hydrocarbon liquid, an aqueousfluid, or a combination thereof, and wherein the acoustic signalcomprises acoustic samples across a portion of the conduit. The one ormore fluids of a plurality of fluids can be introduced into a flowingfluid to determine the inflow signatures for fluid(s) entering flowfluids. In some embodiments, the one or more fluids can be introduced ina relatively stagnant fluid. This may help to model the lower or lowestproducing portion of the well where no bulk fluid flow may be passingthrough the wellbore at the point at which the fluid enters the well.This may be tested to obtain the signature of fluid inflow into a fluidwithin the wellbore that may not be flowing.

The acoustic signal can be obtained by any means known to those of skillin the art. In some embodiments, the acoustic data can be from fielddata where the data is verified by other test instruments. In someembodiments, the acoustic signal is obtained from a sensor within orcoupled to the conduit for each inflow test of the plurality of inflowtests. The sensor can be disposed along the length of the conduit, andthe acoustic signal that is obtained can be indicative of an acousticsource along a length of the conduit. The sensor can comprise a fiberoptic cable disposed within the conduit, or in some embodiments, coupledto the conduit (e.g., on an outside of the conduit). The conduit can bea continuous section of a tubular, and in some embodiments, the can bedisposed in a loop. While described as being a loop in somecircumstances, a single section of pipe or tubular can also be used withadditional piping used to return a portion of the fluid to the entranceof the conduit.

The configuration of the tubular test arrangement can be selected basedon an expected operating configuration. A generic test arrangement maycomprise a single tubular having one or more injection points. Theacoustic sensor can be disposed within the tubular or coupled to anexterior of the tubular. In some embodiments, other arrangement such aspipe-in-pipe arrangements designed to mimic a production tubular in acasing string can be used for the flow tests. The sensor can be disposedwithin the inner pipe, in an annulus between the inner pipe and outerpipe, or coupled to an exterior of the outer pipe. The disposition ofthe sensor and the manner in which it is coupled within the testarrangement can be the same or similar to how it is expected to bedisposed within a wellbore. Any number of testing arrangements andsensor placements can be used, thereby allowing for test datacorresponding to an expected completion configuration. Over time, alibrary of configurations and resulting test data can be developed toallow for future models to be developed based on known, labeled dataused to train multivariate models.

In some embodiments, the conduit comprises a flow loop, and the flowingfluid comprises an aqueous fluid, a hydrocarbon fluid, a gas, or acombination thereof. The flowing fluid can comprise a liquid phase, amulti-phase mixed liquid, or a liquid-gas mixed phase. In someembodiments, the flowing fluid within the conduit can have a flow regimeincluding, but not limited to, laminar flow, plugging flow, sluggingflow, annular flow, turbulent flow, mist flow, bubble flow, or anycombination thereof. Within these flow regimes, the flow and/or inflowcan be time based. For example, a fluid inflow can be laminar over afirst time interval followed by slugging flow over a second time period,followed by a return to laminar or turbulent flow over a third timeperiod. Thus, the specific flow regimes can be interrelated and haveperiodic or non-periodic flow regime changes over time.

An assembly 1 for performing inflow tests is provided in FIG. 8A.Assembly 1 comprises a conduit 5 into or onto which a sensor 2 (e.g., afiber optic cable) is disposed. In some embodiments, the fiber opticcable 2 can be disposed within conduit 5. In some embodiments, the fiberoptic cable 2 can be disposed along an outside of the conduit 5, forexample, coupled to an exterior of the conduit. The fiber optic cablecan be disposed along a length L of conduit 5. In some embodiments,other types of sensors can be used such as point source acoustic orvibration sensors. A line 40 may be configured for introducingbackground fluid into a first end 6 of conduit 5. One or a plurality ofinjection points 10 can be disposed along length L of conduit 5. Anassembly for performing inflow tests can comprise any number ofinjection points. For example, an assembly for performing inflow testsaccording to this disclosure can comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,or more injection points 10. For example, in the embodiment of FIG. 8A,four injection points 10A, 10B, 10C, and 10D are disposed along length Lof conduit 5. By way of example, a length L of conduit 5 may be in arange of from about 10 to about 100 meters, from about 20 to about 80meters, or from about 30 to about 70 meters, for example, 30, 40, 45,50, 55, 60, 65, or 70 meters.

The injection points may be positioned a spacing distance apart withregard to each other and/or first end 6 and second end 7 of conduit 5.The spacing distance can be selected based on a spatial resolution ofthe sensor system such that the injection points can be distinguishedfrom each other in the resulting testing data. When point source sensorsare used, the type of sensors can be considered in selecting the spacingdistance. The spacing distance may also be selected, at least in part,to be sufficient to allow for a desired flow regime to develop betweeninjection points. In some embodiments, first injection point 10A can bepositioned a spacing distance S1 from first end 6 of conduit 5 and asecond spacing S2 from second injection point 10B. Second injectionpoint 10B can be positioned a spacing distance S3 from third injectionpoint 10C. Third injection point 10C can be positioned a spacingdistance S4 from a fourth injection point 10D. Fourth injection point10D can be positioned a spacing distance S5 from a transparent section20 of conduit 5. Transparent section 20 can be utilized to visuallyconfirm the flow regime within conduit 5. The visual appearanceinformation can be recorded as part of the test data set. A productionlogging system (PLS) may be positioned within a spacing distance S6 ofsecond end 7 of conduit 5 and operable to compare data received viasensor or fiber optic cable 2. In some embodiments, without limitation,the spacing distances between injection points (e.g., spacing distancesS2, S3, and S4) are in a range of from about 2 to about 20 m, from about2 to about 15 m, or from about 10 to about 15 m apart. In someembodiments, the first and last injection points are at least 5, 6, 7,8, 9, or 10 m from a closest end (e.g., from first end 6 or second end7) of conduit 5. For example, spacing distances S1 and S5 can be atleast 5, 6, 7, 8, 9, or 10 meters, in embodiments.

The conduit 5 can be disposed at any angle, including any angle between,and including, horizontal to vertical. The angle of the conduit, alongwith the fluid composition and flow rates can affect the flow regimeswithin the conduit. For example, a gas phase may collect along a top ofa horizontally oriented conduit 5 as compared to a bubbling or sluggingflow in a vertical conduit. Thus, the flow regime can change based on anorientation of the conduit even with the same fluid flow rates andcompositions. The angle can be selected to represent those conditionsthat are being modeled to match those found in a wellbore, and the angleof the conduit can become part of the data obtained from the test setup.

Background fluid can be injected into line 40 in any of the flow regimesnoted herein, for example, laminar flow, plugging flow, slugging flow,annular flow, turbulent flow, mist flow, and/or bubble flow, which maybe visually confirmed through transparent section 20 of assembly 1. Thebackground flowing fluid can comprise a liquid phase, a multi-phasemixed liquid, and/or a liquid-gas mixed phase. The inflow tests caninclude various combinations of injected fluid and background flowingfluid. For example, a single phase (e.g., water, gas, or hydrocarbonliquid) can be injected into a background fluid comprising one ormultiple phases (e.g., water, gas, and/or hydrocarbon liquid) flowing ina particular flow regime. Inflow tests can also be performed forinjection of multiphase fluids (e.g., hydrocarbon liquid and gas,hydrocarbon liquid and water, hydrocarbon liquid, water, and gas) into abackground fluid comprising one or multiple phases (e.g., water, gas,and/or hydrocarbon liquid) flowing in a particular flow regime.

In order to understand the variability in the measured signal fortesting purposes, the flow for each type of flow can be incremented overtime. For example, the flow and/or injection rate can be varied in stepsover a time period. Each rate of flow or injection rate can be heldconstant over a time period sufficient to obtain a useable sample dataset. The time period should be sufficient to identify variability in thesignal at a fixed rate. For example, between about 1 minute and about 30minutes of data can be obtained at each stepped flow rate beforechanging the flow rate to a different flow or injection rate.

As depicted in the schematic of FIG. 8B, which is a schematic 3 showingwellbore depths corresponding to injection points of FIG. 8A, the inflowtests can be calibrated to a certain reservoir depth, for example, byadjusting the fiber optic signal for the test depth. For example,injection points 10A, 10B, 10C, and 10D can correspond to inflow depthsD1, D2, D3, and D4, respectively. As an example, a length of fiber opticcable can be used that corresponds to typical wellbore depths (e.g.,3,000 m to 10,000 m, etc.). The resulting acoustic signals can thenrepresent or be approximations of acoustic signals received underwellbore conditions. During the flow tests, acoustic data can beobtained under known flow conditions. The resulting acoustic data canthen be used as training and/or test data for purposes of preparing thefluid flow model. For example, a first portion of the data can be usedwith machine learning techniques to train the fluid flow model, and asecond portion of the data can be used to verify the results from thefluid flow model once it is developed.

Using the test data obtained from the flow apparatus, the method ofdeveloping the fluid flow model can include determining one or morefrequency domain features from the acoustic signal for at least aportion of the data from the plurality of fluid inflow tests. The one ormore frequency domain features can be obtained across the portion of theconduit including the predetermined locations at 910, and training thefluid flow model can use the one or more frequency domain features for aplurality of the tests and the predetermined locations at 920. Thetraining of the fluid flow model can use machine learning, including anysupervised or unsupervised learning approach. For example, the fluidflow model can be a neural network, a Bayesian network, a decision tree,a logistical regression model, a normalized logistical regression model,k-means clustering or the like.

In some embodiments, the fluid flow model can be developed and trainedusing a logistic regression model. As an example for training of a modelused to determine the presence or absence of a hydrocarbon gas phase,the training of the fluid flow model at 920 can begin with providing theone or more frequency domain features to the logistic regression modelcorresponding to one or more inflow tests where the one or more fluidscomprise a hydrocarbon gas. The one or more frequency domain featurescan be provided to the logistic regression model corresponding to one ormore inflow tests where the one or more fluids do not comprise ahydrocarbon gas. A first multivariate model can be determined using theone or more frequency domain features as inputs. The first multivariatemodel can define a relationship between a presence and an absence of thehydrocarbon gas in the one or more fluids.

Similarly, the fluid flow model can include a logistic regression modelfor an aqueous fluid, and the fluid flow model can be trained at 920 byproviding the one or more frequency domain features to the logisticregression model corresponding to one or more inflow tests where the oneor more fluids comprise an aqueous fluid. The one or more frequencydomain features can also be provided to the logistic regression modelcorresponding to one or more inflow tests where the one or more fluidsdo not comprise a aqueous fluid. A second multivariate model can then bedetermined using the one or more frequency domain features as inputswhere the second multivariate model defines a relationship between apresence and an absence of the aqueous fluid in the one or more fluids.

The fluid flow model can also include a logistic regression model forhydrocarbon liquids. Training the fluid flow model at 920 can includeproviding the one or more frequency domain features to the logisticregression model corresponding to one or more inflow tests of theplurality of inflow tests where the one or more fluids comprise ahydrocarbon liquid. One or more frequency domain features can also beprovided to the logistic regression model corresponding to one or moreinflow tests where the one or more fluids do not comprise a hydrocarbonliquid. A third multivariate model can then be determined using the oneor more frequency domain features as inputs, where the thirdmultivariate model defines a relationship between a presence and anabsence of the hydrocarbon liquid in the one or more fluids.

The one or more frequency domain features can comprise any frequencydomain features noted hereinabove as well as combinations andtransformations thereof. For example, In some embodiments, the one ormore frequency domain features comprise a spectral centroid, a spectralspread, a spectral roll-off, a spectral skewness, an RMS band energy, atotal RMS energy, a spectral flatness, a spectral slope, a spectralkurtosis, a spectral flux, a spectral autocorrelation function,combinations and/or transformations thereof, or any normalized variantthereof. In some embodiments, the one or more frequency domain featurescomprise a normalized variant of the spectral spread (NVSS) and/or anormalized variant of the spectral centroid (NVSC).

In the fluid flow model, the multivariate model equations can use thefrequency domain features or combinations or transformations thereof todetermine when a specific fluid or flow regime is present. Themultivariate model can define a threshold, decision point, and/ordecision boundary having any type of shapes such as a point, line,surface, or envelope between the presence and absence of the specificfluid or flow regime. In some embodiments, the multivariate model can bein the form of a polynomial, though other representations are alsopossible. When models such a neural networks are used, the thresholdscan be based on node thresholds within the model. As noted herein, themultivariate model is not limited to two dimensions (e.g., two frequencydomain features or two variables representing transformed values fromtwo or more frequency domain features), and rather can have any numberof variables or dimensions in defining the threshold between thepresence or absence of the fluid or flow regime. When used, the detectedvalues can be used in the multivariate model, and the calculated valuecan be compared to the model values. The presence of the fluid or flowregime can be indicated when the calculated value is on one side of thethreshold and the absence of the fluid or flow regime can be indicatedwhen the calculated value is on the other side of the threshold. Thus,each multivariate model can, in some embodiments, represent a specificdetermination between the presence of absence of a fluid or flow regime.Different multivariate models, and therefore thresholds, can be used foreach fluid and/or flow regime, and each multivariate model can rely ondifferent frequency domain features or combinations or transformationsof frequency domain features. Since the multivariate models definethresholds for the determination and/or identification of specificfluids, the multivariate models and fluid flow model using suchmultivariate models can be considered to be event signatures for eachtype of fluid flow and/or inflow (including flow regimes, etc.).

Once the model is trained or developed, the fluid flow model can beverified or validated. In some embodiments, the plurality of the testsused for training the fluid flow model can be a subset of the pluralityof flow tests, and the tests used to validate the models can be anothersubset of the plurality of flow tests. A method of developing a fluidflow model according to this disclosure can further include thevalidation of the trained fluid flow model using the acoustic signalsfrom one or more tests and the predetermined locations of the one ormore tests at 930.

The validation process can include providing the acoustic signals fromone or more of the plurality of inflow tests and the predeterminedlocations of the one or more of the plurality of inflow tests to each ofthe first multivariate model, the second multivariate model, and thethird multivariate model. A presence or absence of at least one of thegas in the one or more fluids, the aqueous fluid in the one or morefluids, or the hydrocarbon liquid in the one or more fluids based on anoutput of each of the first multivariate model, the second multivariatemodel, and the third multivariate model can then be determined. Thefluid flow model at 930 can be validated by comparing the predictedpresence or absence of the gas in the one or more fluids, the aqueousfluid in the one or more fluids, or the hydrocarbon liquid in the one ormore fluids to the actual presence as known from the test data. Shouldthe accuracy of the fluid flow model be sufficient (e.g., meeting aconfidence threshold), then the fluid flow model can be used to detectand/or identify fluids within a wellbore. If the accuracy is notsufficient, then additional data and training or development can becarried out to either find new frequency domain feature relationships todefine the multivariate models or improve the derived multivariatemodels to more accurately predict the presence and identification of thefluids. In this process, the development, validation, and accuracychecking can be iteratively carried out until a suitable fluid flowmodel is determined. Using the validation process, a confidence levelcan be determined based on the validating at 940; and a remediationprocedure can be performed based on the confidence level at 950.

With reference to FIG. 1, a method of identifying fluid inflow accordingto this disclosure can further comprise determining relative amounts ofgas phase inflow, aqueous phase inflow, and hydrocarbon liquid phaseinflow at 700. Determining relative amounts of gas phase inflow, aqueousphase inflow, and hydrocarbon liquid phase inflow at 700 can comprisedetermining an amplitude of each of the determined at least one of thegas phase inflow, the aqueous phase inflow, or the hydrocarbon liquidphase inflow over a time period at the identified one or more fluidinflow locations; and determining a relative contribution of each of thegas phase inflow, the aqueous phase inflow, or the hydrocarbon liquidphase inflow based on the amplitude of each of the identified at leastone of the gas phase inflow, the aqueous phase inflow, or thehydrocarbon liquid phase inflow over the time period. In someembodiments, the amplitude and/or spectral power of each portion of theacoustic signal can be compared to produced volumes of each fluid. Therelative power originated from various inflow locations can be comparedand assigned a proportion of the overall produced fluid flow based onthe frequency domain features such as the amplitude or spectral power.The volumes of each fluid flowing in the wellbore tubulars can beconfirmed using the fluid flow model, and the relative amountsdetermined at the fluid inflow locations can be used to determine theamounts present in the fluid flow in the wellbore tubulars at pointsdownstream of the inflow locations. This can allow for an estimate ofthe volume of each fluid present at various points in the wellbore to bedetermined.

In an embodiment, a method of identifying fluid flow can comprisedetermining relative amounts of gas phase inflow, aqueous phase inflow,and hydrocarbon liquid phase inflow. Determining relative amounts of gasphase inflow, aqueous phase inflow, and hydrocarbon liquid phase inflowcan comprise determining an amplitude of each of the determined at leastone of the gas phase inflow, the aqueous phase inflow, or thehydrocarbon liquid phase inflow over a time period at the identified oneor more fluid inflow locations; and determining a relative contributionof each of the gas phase inflow, the aqueous phase inflow, or thehydrocarbon liquid phase inflow based on the amplitude of each of theidentified at least one of the gas phase inflow, the aqueous phaseinflow, or the hydrocarbon liquid phase inflow over the time period. Themethod can comprise aggregating the determined amounts of each of gasphase inflow, aqueous phase inflow, and hydrocarbon liquid phase inflowalong the length of the conduit (e.g., the production tubing), anddetermining fluid flow volumes, flow rates, and/or flow regimes withinthe conduit based on the determined amounts of each of gas phase inflow,aqueous phase inflow, and hydrocarbon liquid phase inflow.

A method of identifying fluid inflow according to this disclosure canfurther comprise determining and/or performing a remediation procedureat 800. The remediation procedure determined and/or performed can bebased on the relative amounts of the gas phase inflow, the aqueous phaseinflow, or the hydrocarbon liquid phase inflow at 700, the confidencelevel determined at 650, or a combination thereof.

A combination of the steps discussed herein can be utilized in a methodof identifying fluid inflow according to this disclosure. For example, amethod of determining fluid inflow can comprise obtaining an acousticsignal at 100, determining one or a plurality of frequency domainfeatures from the acoustic signal at 300 and identifying one or morefluid inflow locations from the one or the plurality of frequency domainfeatures at 500. Alternatively, a method of determining fluid inflow cancomprise obtaining an acoustic signal at 100, determining a plurality offrequency domain features from the acoustic signal at 300, identifyingone or more fluid inflow locations from one or more of the plurality offrequency domain features at 500, and identifying at least one of a gasphase inflow, an aqueous phase inflow, or a hydrocarbon liquid phaseinflow at the identified one or more fluid inflow locations using atleast two of the plurality of frequency domain features at 600. Theidentification method can use any of the fluid flow models describedherein. Alternatively, a method of determining fluid inflow can compriseobtaining an acoustic signal at 100, determining a plurality offrequency domain features from the acoustic signal at 300, identifyingone or more fluid inflow locations at 500 (via one or more of the one orthe plurality of frequency domain features or in an alternative manner),and identifying at least one of a gas phase inflow, an aqueous phaseinflow, or a hydrocarbon liquid phase inflow at the identified one ormore fluid inflow locations using at least two of the plurality offrequency domain features at 600. This identification method can use anyof the fluid flow models described herein.

With reference back to FIG. 6, when fluid inflow events have beenidentified as having occurred during the sample data measurement period,which can be in real time or near real time, various outputs can begenerated to display or indicate the presence at 406 of the one or morefluid inflow events that are identified at 500 and/or 600.

In addition to detecting the presence of one or more events (e.g, fluidinflow, gas phase inflow, hydrocarbon liquid phase inflow, aqueous phaseinflow) at a depth or location in the wellbore 114, the analysissoftware executing on the processor 168 can be used to visualize thefluid inflow locations or relative amounts over a computer network forvisualization on a remote location. For example, as depicted in FIG. 9,an output can comprise one or more of a plot of the gas phase inflow asa function of depth in the well and time as depicted in panel A, a plotof the hydrocarbon liquid phase inflow as a function of depth in thewell and time as depicted in panel B, a plot of the aqueous phase inflowas a function of depth in the well and time, as depicted in panel C. Theplots can be overlaid to provide a single plot depicting the gas phaseinflow, aqueous phase inflow, and hydrocarbon liquid phase inflow as afunction of depth in the well and time, as depicted in panel D of FIG.9. Alternatively or additionally, the data can be integrated to providea cumulative display of the amounts of gas phase inflow, aqueous phaseinflow, and hydrocarbon liquid phase inflow as a function of depth inthe well and time, as depicted in panel E of FIG. 9.

The computation of a fluid inflow event log may be done repeatedly, suchas every second, and later integrated/averaged for discrete timeperiods—for instance, at times of higher well drawdowns, to display atime-lapsed event log at various stages of the production process (e.g.,from baseline shut-in, from during well ramp-up, from steady production,from high drawdown/production rates etc.). The time intervals may belong enough to provide suitable data, though longer times may result inlarger data sets. In an embodiment, the time integration may occur overa time period between about 0.1 seconds to about 10 seconds, or betweenabout 0.5 seconds and about a few minutes or even hours.

The resulting fluid inflow event log(s) computed every second can bestored in the memory 170 or transferred across a computer network, topopulate a fluid inflow event database. The data can be used to generatean integrated fluid inflow event log at each fluid inflow event depthsample point along the length of the optical fiber 162 along with asynchronized timestamp that indicates the times of measurement. Inproducing a visualization fluid inflow event log, the values for depthsections that do not exhibit fluid inflow can be set to zero. Thisallows those depth points or zones exhibiting fluid inflow to be easilyidentified.

As an example, the analysis software executing on the processor 168 canbe used to visualize fluid inflow locations or relative fluid inflowamounts over a computer network for visualization on a remote location.The computation of a ‘fluid inflow log’ may be done repeatedly, such asevery second, and later integrated/averaged for discrete timeperiods—for instance, at times of higher well drawdowns, to display atime-lapsed fluid inflow log at various stages of the production process(e.g., from baseline shut-in, from during well ramp-up, from steadyproduction, from high drawdown/production rates etc.). The timeintervals may be long enough to provide suitable data, though longertimes may result in larger data sets. In an embodiment, the timeintegration may occur over a time period between about 0.1 seconds toabout 10 seconds, or between about 0.5 seconds and about a few minutesor even hours.

Fluid inflow logs computed every second can be stored in the memory 170or transferred across a computer network, to populate an event database.The data stored/transferred in the memory 170 for one or more of thedata set depths may be stored every second. This data can be used togenerate an integrated fluid inflow log at each event depth sample pointalong the length of the optical fiber 162 along with a synchronizedtimestamp that indicates the times of measurement.

The data output by the system may generally indicate one or more fluidinflow locations or depths, a composition (e.g., oil, gas, water) of theinflowing fluid at the indicated fluid inflow locations, and optionally,a relative amount of fluid inflow component(s) among the identifiedlocations or depths and/or a qualitative indicator of fluid inflowcomponent(s) entering the wellbore at a location. The data output canalso indicate fluid flow locations within the wellbore tubulars andfluid compositions at those locations.

38 If water or gas inflow is observed in the produced fluid (asdetermined by methods such as surface detectors, visual observation,etc.), but the location and/or amount of the water or gas inflow cannotbe identified with sufficient clarity using the methods describedherein, various actions can be taken in order to obtain a bettervisualization of the acoustic data. In an embodiment, the productionrate can be temporarily increased. The resulting data analysis can beperformed on the data during the increased production period. Ingeneral, an increased fluid flow rate into the wellbore may be expectedto increase the acoustic signal intensity at the fluid inflow locations.This may allow a signal to noise ratio to be improved in order to moreclearly identify fluid flow and/or inflow at one or more locations by,for example, providing for an increased signal strength. The water, gas,and/or hydrocarbon liquid flow and/or inflow energies can also be moreclearly calculated based on the increased signal outputs. Once the zonesof interest are identified, the production levels can be adjusted basedon the water or gas inflow locations and amounts. Any changes in waterand/or gas production amounts over time can be monitored using thetechniques described herein and the operating conditions can be adjustedaccordingly (e.g., dynamically adjusted, automatically adjusted,manually adjusted, etc.).

In some embodiments, the change in the production rate can be used todetermine a production rate correlation with the fluid inflow locationsand flow rates at one or more points along the wellbore. In general,decreasing the production rate may be expected to reduce the fluidinflow rates and fluid flow rates. By determining production ratecorrelations with the fluid inflow rates, the production rate from thewell and/or one or more zones can be adjusted to reduce the fluid inflowrate at the identified locations. For example, an adjustable productionsleeve or choke can be altered to adjust specific fluid inflow rates inone or more production zones. If none of the production zones areadjustable, various workover procedures can be used to alter theproduction from specific zones. For example, various intake sleeves canbe blocked off, zonal isolation devices can be used to block offproduction from certain zones, and/or some other operations can becarried out to reduce the amount of undesired fluid inflow (e.g.,consolidation procedures, etc.).

The same analysis procedure can be used with any of the fluid flowand/or inflow event signatures described herein. For example, thepresence of one or more fluid inflow events (e.g., fluid inflow, gasinflow, water inflow, hydrocarbon liquid inflow) can be determined. Insome embodiments, the location and or discrimination between events maynot be clear. One or more characteristics of the wellbore can then bechanged to allow a second measurement of the acoustic signal to occur.For example, the production rate can be changed, the pressures can bechanged, one or more zones can be shut-in, or any other suitableproduction change. For example, the production rate can be temporarilyincreased. The resulting data analysis can be performed on the dataduring the increased production period. In general, an increased fluidflow rate into the wellbore may be expected to increase the acousticsignal intensity at certain event locations such as a gas inflowlocation, a water inflow location, a hydrocarbon liquid inflow location,or the like. This may allow a signal to noise ratio to be improved inorder to more clearly identify one event relative to another at one ormore locations by, for example, providing for an increased signalstrength to allow the event signatures to be compared to the resultingacoustic signal. The event energies can also be more clearly calculatedbased on the increased signal outputs. Once the zones of interest areidentified, the production levels can be adjusted based on the eventlocations and amounts. Any changes in the presence of the fluid inflowevents over time can be monitored using the techniques described hereinand the operating conditions can be adjusted accordingly (e.g.,dynamically adjusted, automatically adjusted, manually adjusted, etc.).While the data analysis has been described above with respect to thesystem 101, methods of identifying events within the wellbore (e.g.,fluid inflow locations along the length of a wellbore, phasediscrimination (e.g., gas, water, hydrocarbon liquid) of inflowingfluid, relative amounts of inflowing fluid components, etc.) can also becarried out using any suitable system. For example, the system of FIG. 2can be used to carry out the acoustic data acquisition, a separatesystem at a different time and/or location can be used with acousticdata to perform the fluid inflow event identification method, and/or themethod can be performed using acoustic data obtained from a differenttype of acoustic sensor where the data is obtained in an electronic formuseable with a device capable of performing the method.

The acoustic signal can include data for all of the wellbore or only aportion of the wellbore. An acoustic sample data set can be obtainedfrom the acoustic signal. In an embodiment, the sample data set mayrepresent a portion of the acoustic signal for a defined depth range orpoint. In some embodiments, the acoustic signal can be obtained in thetime domain. For example, the acoustic signal may be in the form of anacoustic amplitude relative to a collection time. The sample data setmay also be in the time domain and be converted into the frequencydomain using a suitable transform such as a Fourier transform. In someembodiments, the sample data set can be obtained in the frequency domainsuch that the acoustic signal can be converted prior to obtaining thesample data set. While the sample data set can be obtained using any ofthe methods described herein, the sample data set can also be obtainedby receiving it from another device. For example, a separate extractionor processing step can be used to prepare one or more sample data setsand transmit them for separate processing using any of the processingmethods or systems disclosed herein.

Any of the systems and methods disclosed herein can be carried out on acomputer or other device comprising a processor, such as the acquisitiondevice 160 of FIG. 2. FIG. 10 illustrates a computer system 780 suitablefor implementing one or more embodiments disclosed herein such as theacquisition device or any portion thereof. The computer system 780includes a processor 782 (which may be referred to as a centralprocessor unit or CPU) that is in communication with memory devicesincluding secondary storage 784, read only memory (ROM) 786, randomaccess memory (RAM) 788, input/output (I/O) devices 790, and networkconnectivity devices 792. The processor 782 may be implemented as one ormore CPU chips.

It is understood that by programming and/or loading executableinstructions onto the computer system 780, at least one of the CPU 782,the RAM 788, and the ROM 786 are changed, transforming the computersystem 780 in part into a particular machine or apparatus having thenovel functionality taught by the present disclosure. It is fundamentalto the electrical engineering and software engineering arts thatfunctionality that can be implemented by loading executable softwareinto a computer can be converted to a hardware implementation bywell-known design rules. Decisions between implementing a concept insoftware versus hardware typically hinge on considerations of stabilityof the design and numbers of units to be produced rather than any issuesinvolved in translating from the software domain to the hardware domain.Generally, a design that is still subject to frequent change may bepreferred to be implemented in software, because re-spinning a hardwareimplementation is more expensive than re-spinning a software design.Generally, a design that is stable that will be produced in large volumemay be preferred to be implemented in hardware, for example in anapplication specific integrated circuit (ASIC), because for largeproduction runs the hardware implementation may be less expensive thanthe software implementation. Often a design may be developed and testedin a software form and later transformed, by well-known design rules, toan equivalent hardware implementation in an application specificintegrated circuit that hardwires the instructions of the software. Inthe same manner as a machine controlled by a new ASIC is a particularmachine or apparatus, likewise a computer that has been programmedand/or loaded with executable instructions may be viewed as a particularmachine or apparatus.

Additionally, after the system 780 is turned on or booted, the CPU 782may execute a computer program or application. For example, the CPU 782may execute software or firmware stored in the ROM 786 or stored in theRAM 788. In some cases, on boot and/or when the application isinitiated, the CPU 782 may copy the application or portions of theapplication from the secondary storage 784 to the RAM 788 or to memoryspace within the CPU 782 itself, and the CPU 782 may then executeinstructions of which the application is comprised. In some cases, theCPU 782 may copy the application or portions of the application frommemory accessed via the network connectivity devices 792 or via the I/Odevices 790 to the RAM 788 or to memory space within the CPU 782, andthe CPU 782 may then execute instructions of which the application iscomprised. During execution, an application may load instructions intothe CPU 782, for example load some of the instructions of theapplication into a cache of the CPU 782. In some contexts, anapplication that is executed may be said to configure the CPU 782 to dosomething, e.g., to configure the CPU 782 to perform the function orfunctions promoted by the subject application. When the CPU 782 isconfigured in this way by the application, the CPU 782 becomes aspecific purpose computer or a specific purpose machine.

The secondary storage 784 is typically comprised of one or more diskdrives or tape drives and is used for non-volatile storage of data andas an over-flow data storage device if RAM 788 is not large enough tohold all working data. Secondary storage 784 may be used to storeprograms which are loaded into RAM 788 when such programs are selectedfor execution. The ROM 786 is used to store instructions and perhapsdata which are read during program execution. ROM 786 is a non-volatilememory device which typically has a small memory capacity relative tothe larger memory capacity of secondary storage 784. The RAM 788 is usedto store volatile data and perhaps to store instructions. Access to bothROM 786 and RAM 788 is typically faster than to secondary storage 784.The secondary storage 784, the RAM 788, and/or the ROM 786 may bereferred to in some contexts as computer readable storage media and/ornon-transitory computer readable media.

I/O devices 790 may include printers, video monitors, liquid crystaldisplays (LCDs), touch screen displays, keyboards, keypads, switches,dials, mice, track balls, voice recognizers, card readers, paper tapereaders, or other well-known input devices.

The network connectivity devices 792 may take the form of modems, modembanks, Ethernet cards, universal serial bus (USB) interface cards,serial interfaces, token ring cards, fiber distributed data interface(FDDI) cards, wireless local area network (WLAN) cards, radiotransceiver cards that promote radio communications using protocols suchas code division multiple access (CDMA), global system for mobilecommunications (GSM), long-term evolution (LTE), worldwideinteroperability for microwave access (WiMAX), near field communications(NFC), radio frequency identity (RFID), and/or other air interfaceprotocol radio transceiver cards, and other well-known network devices.These network connectivity devices 792 may enable the processor 782 tocommunicate with the Internet or one or more intranets. With such anetwork connection, it is contemplated that the processor 782 mightreceive information from the network, or might output information to thenetwork (e.g., to an event database) in the course of performing theabove-described method steps. Such information, which is oftenrepresented as a sequence of instructions to be executed using processor782, may be received from and outputted to the network, for example, inthe form of a computer data signal embodied in a carrier wave.

Such information, which may include data or instructions to be executedusing processor 782 for example, may be received from and outputted tothe network, for example, in the form of a computer data baseband signalor signal embodied in a carrier wave. The baseband signal or signalembedded in the carrier wave, or other types of signals currently usedor hereafter developed, may be generated according to several methodswell-known to one skilled in the art. The baseband signal and/or signalembedded in the carrier wave may be referred to in some contexts as atransitory signal.

The processor 782 executes instructions, codes, computer programs,scripts which it accesses from hard disk, floppy disk, optical disk(these various disk based systems may all be considered secondarystorage 784), flash drive, ROM 786, RAM 788, or the network connectivitydevices 792. While only one processor 782 is shown, multiple processorsmay be present. Thus, while instructions may be discussed as executed bya processor, the instructions may be executed simultaneously, serially,or otherwise executed by one or multiple processors. Instructions,codes, computer programs, scripts, and/or data that may be accessed fromthe secondary storage 784, for example, hard drives, floppy disks,optical disks, and/or other device, the ROM 786, and/or the RAM 788 maybe referred to in some contexts as non-transitory instructions and/ornon-transitory information.

In an embodiment, the computer system 780 may comprise two or morecomputers in communication with each other that collaborate to perform atask. For example, but not by way of limitation, an application may bepartitioned in such a way as to permit concurrent and/or parallelprocessing of the instructions of the application. Alternatively, thedata processed by the application may be partitioned in such a way as topermit concurrent and/or parallel processing of different portions of adata set by the two or more computers. In an embodiment, virtualizationsoftware may be employed by the computer system 780 to provide thefunctionality of a number of servers that is not directly bound to thenumber of computers in the computer system 780. For example,virtualization software may provide twenty virtual servers on fourphysical computers. In an embodiment, the functionality disclosed abovemay be provided by executing the application and/or applications in acloud computing environment. Cloud computing may comprise providingcomputing services via a network connection using dynamically scalablecomputing resources. Cloud computing may be supported, at least in part,by virtualization software. A cloud computing environment may beestablished by an enterprise and/or may be hired on an as-needed basisfrom a third party provider. Some cloud computing environments maycomprise cloud computing resources owned and operated by the enterpriseas well as cloud computing resources hired and/or leased from a thirdparty provider.

In an embodiment, some or all of the functionality disclosed above maybe provided as a computer program product. The computer program productmay comprise one or more computer readable storage medium havingcomputer usable program code embodied therein to implement thefunctionality disclosed above. The computer program product may comprisedata structures, executable instructions, and other computer usableprogram code. The computer program product may be embodied in removablecomputer storage media and/or non-removable computer storage media. Theremovable computer readable storage medium may comprise, withoutlimitation, a paper tape, a magnetic tape, magnetic disk, an opticaldisk, a solid state memory chip, for example analog magnetic tape,compact disk read only memory (CD-ROM) disks, floppy disks, jump drives,digital cards, multimedia cards, and others. The computer programproduct may be suitable for loading, by the computer system 780, atleast portions of the contents of the computer program product to thesecondary storage 784, to the ROM 786, to the RAM 788, and/or to othernon-volatile memory and volatile memory of the computer system 780. Theprocessor 782 may process the executable instructions and/or datastructures in part by directly accessing the computer program product,for example by reading from a CD-ROM disk inserted into a disk driveperipheral of the computer system 780. Alternatively, the processor 782may process the executable instructions and/or data structures byremotely accessing the computer program product, for example bydownloading the executable instructions and/or data structures from aremote server through the network connectivity devices 792. The computerprogram product may comprise instructions that promote the loadingand/or copying of data, data structures, files, and/or executableinstructions to the secondary storage 784, to the ROM 786, to the RAM788, and/or to other non-volatile memory and volatile memory of thecomputer system 780.

In some contexts, the secondary storage 784, the ROM 786, and the RAM788 may be referred to as a non-transitory computer readable medium or acomputer readable storage media. A dynamic RAM embodiment of the RAM788, likewise, may be referred to as a non-transitory computer readablemedium in that while the dynamic RAM receives electrical power and isoperated in accordance with its design, for example during a period oftime during which the computer system 780 is turned on and operational,the dynamic RAM stores information that is written to it. Similarly, theprocessor 782 may comprise an internal RAM, an internal ROM, a cachememory, and/or other internal non-transitory storage blocks, sections,or components that may be referred to in some contexts as non-transitorycomputer readable media or computer readable storage media.

Having described various systems and methods herein, specificembodiments can include, but are not limited to:

In a first embodiment, a method of identifying inflow locations along awellbore comprises: obtaining an acoustic signal from a sensor withinthe wellbore, wherein the acoustic signal comprises acoustic samplesacross a portion of a depth of the wellbore; determining a plurality offrequency domain features from the acoustic signal, wherein theplurality of frequency domain features are obtained across a pluralityof depth intervals within the portion of the depth of the wellbore, andwherein the plurality of frequency domain features comprise at least twodifferent frequency domain features; and identifying at least one of agas phase inflow, an aqueous phase inflow, or a hydrocarbon liquid phaseinflow using the plurality of the frequency domain features at one ormore fluid inflow locations.

A second embodiment can include the method of the first embodiment,further comprising: identifying the one or more fluid inflow locationswithin the plurality of depth intervals using one or more frequencydomain features of the plurality of frequency domain features.

A third embodiment can include the method of any one of the first or thesecond embodiment, wherein the one or more frequency domain featurescomprise a spectral flatness.

A fourth embodiment can include the method of any one of the firstthrough the third embodiments, wherein the sensor comprises a fiberoptic cable disposed within the wellbore.

A fifth embodiment can include the method of any one of the firstthrough the fourth embodiments, wherein the plurality of frequencydomain features comprises at least two of: a spectral centroid, aspectral spread, a spectral roll-off, a spectral skewness, an RMS bandenergy, a total RMS energy, a spectral flatness, a spectral slope, aspectral kurtosis, a spectral flux, a spectral autocorrelation function,or a normalized variant thereof.

A sixth embodiment can include the method of any one of the firstthrough the fifth embodiments, further comprising: denoising theacoustic signal prior to determining the plurality of frequency domainfeatures.

A seventh embodiment can include the method of the sixth embodiment,wherein denoising the acoustic signal comprises median filtering theacoustic data.

An eighth embodiment can include the method of any of the first throughthe seventh embodiments, further comprising: calibrating the acousticsignal.

A ninth embodiment can include the method of any one of the firstthrough the eighth embodiments, further comprising: normalizing the oneor more frequency domain features prior to identifying the one or moreinflow locations using the one or more frequency domain features.

A tenth embodiment can include the method of any one of the firstthrough the ninth embodiments, wherein identifying the one or more fluidinflow locations comprises: identifying a background fluid flowsignature using the acoustic signal; and removing the background fluidflow signature from the acoustic signal prior to identifying the one ormore fluid inflow locations.

An eleventh embodiment can include the method of any one of the firstthrough the ninth embodiments, wherein identifying the one or more fluidinflow locations comprises: identifying one or more anomalies in theacoustic signal using the one or more frequency domain features of theplurality of frequency domain features; and selecting the depthintervals of the one or more anomalies as the one or more inflowlocations.

A twelfth embodiment can include any one of the first through theeleventh embodiments, wherein identifying at least one of the gas phaseinflow, the aqueous phase inflow, or the hydrocarbon liquid phase inflowcomprises: providing the plurality of frequency domain features to alogistic regression model for each of the gas phase inflow, the aqueousphase inflow, and the hydrocarbon liquid phase inflow; and determiningthat at least one of the gas phase inflow, the aqueous phase inflow, orthe hydrocarbon liquid phase inflow is present based on the logisticregression model.

A thirteenth embodiment can include the method of the twelfthembodiment, wherein the logistic regression model uses a firstmultivariate model having the plurality of frequency domain features asinputs to determine when the gas phase inflow is present, wherein thelogistic regression model uses a second multivariate model having theplurality of frequency domain features as inputs to determine when theaqueous phase inflow is present, and wherein the logistic regressionmodel uses a third multivariate model having the plurality of frequencydomain features as inputs to determine when the hydrocarbon liquid phaseinflow is present.

A fourteenth embodiment can include the method of any one of the firstthrough the thirteenth embodiments, further comprising: determining anamplitude of each of the identified at least one of the gas phaseinflow, the aqueous phase inflow, or the hydrocarbon liquid phase inflowover a time period; and determining a relative contribution of each ofthe gas phase inflow, the aqueous phase inflow, or the hydrocarbonliquid phase inflow based on the amplitude of each of the identified atleast one of the gas phase inflow, the aqueous phase inflow, or thehydrocarbon liquid phase inflow over the time period.

A fifteenth embodiment can include the method of the fourteenthembodiment, further comprising: determining a remediation procedurebased on the relative contribution of each of the gas phase inflow, theaqueous phase inflow, or the hydrocarbon liquid phase inflow; andperforming the remediation procedure.

A sixteenth embodiment can include the method of any one of the twelfththrough the fifteenth embodiments, wherein the plurality of frequencydomain features comprise a normalized variant of the spectral spread anda normalized variant of the spectral centroid, and wherein the logisticregression model defines a relationship between a presence or absence ofthe gas phase inflow, the aqueous phase inflow, or the hydrocarbonliquid phase inflow.

A seventeenth embodiment can include the method of any one of the firstthrough the sixteenth embodiments, further comprising: determining aconfidence level for the identification of the at least one of the gasphase inflow, the aqueous phase inflow, or the hydrocarbon liquid phaseinflow; and performing a remediation procedure based on the confidencelevel.

An eighteenth embodiment can include the method of any one of the firstthrough the seventeenth embodiments, wherein obtaining the acousticsignal from the sensor within the wellbore occurs from between 30minutes and 4 hours.

A nineteenth embodiment can include the method of any one of the firstthrough the eighteenth embodiments, wherein the sensor comprises a fiberoptic cable disposed within a production tubing within the wellbore.

A twentieth embodiment can include the method of any one of the firstthrough the nineteenth embodiments, wherein identifying the at least oneof the gas phase inflow, the aqueous phase inflow, or the hydrocarbonliquid phase inflow using the plurality of the frequency domain featurescomprises: identifying the at least one of the gas phase inflow, theaqueous phase inflow, or the hydrocarbon liquid phase inflow using aderivative of at least one of the plurality of the frequency domainfeatures.

A twenty-first embodiment can include the method of any one of the firstthrough the twentieth embodiments, wherein identifying the at least oneof the gas phase inflow, the aqueous phase inflow, or the hydrocarbonliquid phase inflow using the plurality of the frequency domain featurescomprises: identifying the at least one of the gas phase inflow, theaqueous phase inflow, or the hydrocarbon liquid phase inflow using aratio between at least two of the plurality of the frequency domainfeatures.

In a twenty-second embodiment, a method of developing an inflow locationmodel for a wellbore can comprise: performing a plurality of inflowtests, wherein each inflow test comprises introducing one or more fluidsof a plurality of fluids into a flowing fluid within a conduit atpredetermined locations, and wherein the plurality of fluids comprise ahydrocarbon gas, a hydrocarbon liquid, an aqueous fluid, or acombination thereof; obtaining an acoustic signal from a sensor withinthe conduit for each inflow test of the plurality of inflow tests,wherein the acoustic signal comprises acoustic samples across a portionof the conduit including the predetermined locations; determining one ormore frequency domain features from the acoustic signal for each of theplurality of inflow tests, wherein the one or more frequency domainfeatures are obtained across the portion of the conduit including thepredetermined locations; and training a fluid flow model using the oneor more frequency domain features for a plurality of the tests and thepredetermined locations.

A twenty-third embodiment can include the method of the twenty-secondembodiment, further comprising: validating the fluid flow model usingthe acoustic signals from one or more of the tests and the predeterminedlocations of the plurality of tests.

A twenty-fourth embodiment can include the method of the twenty-secondor the twenty-third embodiment, wherein the conduit comprises a flowloop, and wherein the flowing fluid comprises an aqueous fluid, ahydrocarbon fluid, a gas, or a combination thereof.

A twenty-fifth embodiment can include the method of any one of thetwenty-second through the twenty-fourth embodiments, wherein the flowingfluid comprises a liquid phase, a multi-phase mixed liquid, or aliquid-gas mixed phase.

A twenty-sixth embodiment can include the method of any one of thetwenty-second through the twenty-fifth embodiments, wherein theplurality of the tests used for training the fluid flow model is asubset of the plurality of inflow tests.

A twenty-seventh embodiment can include the method of any one of thetwenty-second through the twenty-sixth embodiments, wherein the fluidflow model comprises a logistic regression model, and wherein trainingthe fluid flow model comprises: providing the one or more frequencydomain features to the logistic regression model corresponding to one ormore inflow tests of the plurality of inflow tests where the one or morefluids comprise a hydrocarbon gas; providing the one or more frequencydomain features to the logistic regression model corresponding to one ormore inflow tests of the plurality of inflow tests where the one or morefluids do not comprise a hydrocarbon gas; and determining a firstmultivariate model using the one or more frequency domain features asinputs, wherein the first multivariate model defines a relationshipbetween a presence and an absence of the hydrocarbon gas in the one ormore fluids.

A twenty-eighth embodiment ca include the method of any one of thetwenty-second through the twenty-seventh embodiments, wherein the fluidflow model comprises a logistic regression model, and wherein trainingthe fluid flow model comprises: providing the one or more frequencydomain features to the logistic regression model corresponding to one ormore inflow tests of the plurality of inflow tests where the one or morefluids comprise an aqueous fluid; providing the one or more frequencydomain features to the logistic regression model corresponding to one ormore inflow tests of the plurality of inflow tests where the one or morefluids do not comprise a aqueous fluid; and determining a secondmultivariate model using the one or more frequency domain features asinputs, wherein the second multivariate model defines a relationshipbetween a presence and an absence of the aqueous fluid in the one ormore fluids.

A twenty-ninth embodiment can include the method of any one of thetwenty-second through the twenty-eighth embodiments, wherein the fluidflow model comprises a logistic regression model, and wherein trainingthe fluid flow model comprises: providing the one or more frequencydomain features to the logistic regression model corresponding to one ormore inflow tests of the plurality of inflow tests where the one or morefluids comprise a hydrocarbon liquid; providing the one or morefrequency domain features to the logistic regression model correspondingto one or more inflow tests of the plurality of inflow tests where theone or more fluids do not comprise a hydrocarbon liquid; and determininga third multivariate model using the one or more frequency domainfeatures as inputs, wherein the third multivariate model defines arelationship between a presence and an absence of the hydrocarbon liquidin the one or more fluids.

A thirtieth embodiment can include the method of any one of thetwenty-second through the twenty-ninth embodiments, wherein the one ormore frequency domain features comprise a normalized variant of thespectral spread (NVSS) and a normalized variant of the spectral centroid(NVSC).

A thirty-first embodiment can include the method of any one of thetwenty-ninth or the thirtieth embodiments, further comprising: providingthe acoustic signals from one or more of the plurality of inflow testsand the predetermined locations of the plurality of tests to each of thefirst multivariate model, the second multivariate model, and the thirdmultivariate model; determining a presence of at least one of the gas inthe one or more fluids, the aqueous fluid in the one or more fluids, orthe hydrocarbon liquid in the one or more fluids based on an output ofeach of the first multivariate model, the second multivariate model, andthe third multivariate model; and validating the fluid flow model usingat least a portion of the plurality of inflow tests, the predeterminedlocations of the plurality of tests, and the presence of at least one ofthe gas in the one or more fluids, an aqueous fluid in the one or morefluids, or the hydrocarbon liquid in the one or more fluids asdetermined from the first multivariate model, the second multivariatemodel, and the third multivariate model.

A thirty-second embodiment can include the method of the thirty-firstembodiment, further comprising: determining a confidence level based onthe validating; and performing a remediation procedure based on theconfidence level.

A thirty-third embodiment can include the method of any one of thetwenty-second through the twenty-sixth embodiments, wherein the fluidflow model is a neural network, a Bayesian network, a decision tree, asupervised learning algorithm, a logistical regression model, or anormalized logistical regression model.

A thirty-fourth embodiment can include the method of any one of thetwenty-second through the thirty-third embodiments, wherein the conduitis disposed in a loop.

A thirty-fifth embodiment can include the method of any one of thetwenty-second through the thirty-fourth embodiments, wherein the sensorcomprises a fiber optic cable disposed within the conduit.

A thirty-sixth embodiment can include the method of any one of thetwenty-second through the thirty-fifth embodiments, wherein the flowingfluid within the conduit has a flow regime selected from the groupconsisting of: laminar flow, plugging flow, slugging flow, annular flow,turbulent flow, mist flow, and bubble flow.

A thirty-seventh embodiment can include the method of any one of thetwenty-second through the thirty-sixth embodiments, wherein the sensoris disposed along the length of the conduit, and wherein the acousticsignal is indicative of an acoustic source along a length of theconduit.

In a thirty-eighth embodiment, a method a method of characterizing fluidinflow into a wellbore comprises: obtaining an acoustic signal from asensor within the wellbore, wherein the acoustic signal comprisesacoustic samples across a portion of a depth of the wellbore;determining a plurality of frequency domain features from the acousticsignal, wherein the plurality of frequency domain features are obtainedacross a plurality of depth intervals within the portion of the depth ofthe wellbore, and wherein the plurality of frequency domain featurescomprise at least two different frequency domain features; identifyingone or more fluid inflow locations within the plurality of depthintervals using one or more frequency domain features of the pluralityof frequency domain features; providing the plurality of frequencydomain features at the identified one or more fluid inflow locations toa fluid flow model; and determining at least one of a gas phase inflow,an aqueous phase inflow, or a hydrocarbon liquid phase inflow at theidentified one or more fluid inflow locations using the fluid flowmodel.

A thirty-ninth embodiment can include the method of the thirty-eighthembodiment, further comprising: determining an amplitude of each of thedetermined at least one of the gas phase inflow, the aqueous phaseinflow, or the hydrocarbon liquid phase inflow over a time period at theidentified one or more fluid inflow locations; and determining arelative contribution of each of the gas phase inflow, the aqueous phaseinflow, or the hydrocarbon liquid phase inflow based on the amplitude ofeach of the identified at least one of the gas phase inflow, the aqueousphase inflow, or the hydrocarbon liquid phase inflow over the timeperiod.

A fortieth embodiment can include the method of the thirty-eighth or thethirty-ninth embodiments, wherein the fluid flow model comprises alogistic regression model, and wherein determining at least one of thegas phase inflow, the aqueous phase inflow, or the hydrocarbon liquidphase using the fluid flow model comprises: providing the plurality offrequency domain features to the logistic regression model for each ofthe gas phase inflow, the aqueous phase inflow, and the hydrocarbonliquid phase inflow; and determining that at least one of the gas phaseinflow, the aqueous phase inflow, or the hydrocarbon liquid phase inflowis present based on the logistic regression model.

A forty-first embodiment can include the method of the fortiethembodiment, wherein the logistic regression model uses a firstmultivariate model having the plurality of frequency domain features asinputs to determine when the gas phase inflow is present, wherein thelogistic regression model uses a second multivariate model having theplurality of frequency domain features as inputs to determine when theaqueous phase inflow is present, and wherein the logistic regressionmodel uses a third multivariate model having the plurality of frequencydomain features as inputs to determine when the hydrocarbon liquid phaseinflow is present.

A forty-second embodiment can include the method of the forty-firstembodiment, wherein determining at least one of the gas phase inflow,the aqueous phase inflow, or the hydrocarbon liquid phase using thefluid flow model comprises: using the plurality of frequency domainfeatures at the identified one or more fluid inflow locations in thefirst multivariate model; using the plurality of frequency domainfeatures at the identified one or more fluid inflow locations in thesecond multivariate model; using the plurality of frequency domainfeatures at the identified one or more fluid inflow locations in thethird multivariate model; comparing the plurality of frequency domainfeatures to an output of the first multivariate model, an output of thesecond multivariate model, and an output of the third multivariatemodel; and identifying at least one of the gas phase inflow, the aqueousphase inflow, or the hydrocarbon liquid phase inflow based on thecomparison of the plurality of frequency domain features to the outputof the first multivariate model, the output of the second multivariatemodel, and the output of the third multivariate model.

A forty-third embodiment can include the method of any one of thethirty-eighth through the forty-second embodiments, wherein the one ormore frequency domain features comprise a normalized frequency domainfeature.

A forty-fourth embodiment can include the method of any one of thethirty-eighth through the forty-third embodiments, wherein identifyingthe one or more fluid inflow locations within the plurality of depthintervals using the one or more frequency domain features comprises:identifying the one or more fluid inflow locations within the pluralityof depth intervals using a ratio of two or more frequency domainfeatures.

A forty-fifth embodiment can include the method of any one of thethirty-eighth through the forty-fourth embodiments, further comprising:calibrating the acoustic signal from the sensor prior to determining theplurality of frequency domain features.

A forty-sixth embodiment can include the method of the forty-fifthembodiment, wherein calibrating the acoustic signal comprises: removinga background signal from the acoustic signal.

A forty-seventh embodiment can include the method of the forty-fifthembodiment, wherein calibrating the acoustic signal comprises:identifying one or more anomalies within the acoustic signal; andremoving one or more portions of the acoustic signal outside of the oneor more anomalies.

A forty-eighth embodiment can include a method of identifying inflowlocations along a wellbore, the method comprising: obtaining an acousticsignal from a sensor within the wellbore, wherein the acoustic signalcomprises acoustic samples across a portion of a depth of the wellbore;determining one or more frequency domain features from the acousticsignal, wherein the one or more frequency domain features are obtainedacross a plurality of depth intervals within the portion of the depth ofthe wellbore; and identifying one or more fluid inflow locations withinthe plurality of depth intervals using the one or more frequency domainfeatures.

A forty-ninth embodiment can include the method of the forty-eighthembodiment, further comprising identifying at least one of a gas phaseinflow, an aqueous phase inflow, or a hydrocarbon liquid phase inflow atleast two of the one or more frequency domain features at one or more ofthe one or more identified fluid inflow locations.

A fiftieth embodiment can include the method of the forty-ninthembodiment, wherein the at least two frequency domain features areselected from a spectral centroid, a spectral spread, a spectralroll-off, a spectral skewness, an RMS band energy, a total RMS energy, aspectral flatness, a spectral slope, a spectral kurtosis, a spectralflux, a spectral autocorrelation function, or a normalized variantthereof.

A fifty-first embodiment can include the method of any of theforty-eighth through the fiftieth embodiments, wherein identifying theone or more fluid inflow locations within the plurality of depthintervals using a single one of the one or more frequency domainfeatures.

A fifty-second embodiment can include the method of the fifty-firstembodiment, wherein the single one of the one or more frequency domainfeatures comprises the RMS band energy or a spectral flatness.

While various embodiments in accordance with the principles disclosedherein have been shown and described above, modifications thereof may bemade by one skilled in the art without departing from the spirit and theteachings of the disclosure. The embodiments described herein arerepresentative only and are not intended to be limiting. Manyvariations, combinations, and modifications are possible and are withinthe scope of the disclosure. Alternative embodiments that result fromcombining, integrating, and/or omitting features of the embodiment(s)are also within the scope of the disclosure. Accordingly, the scope ofprotection is not limited by the description set out above, but isdefined by the claims which follow, that scope including all equivalentsof the subject matter of the claims. Each and every claim isincorporated as further disclosure into the specification and the claimsare embodiment(s) of the present invention(s). Furthermore, anyadvantages and features described above may relate to specificembodiments, but shall not limit the application of such issued claimsto processes and structures accomplishing any or all of the aboveadvantages or having any or all of the above features.

Additionally, the section headings used herein are provided forconsistency with the suggestions under 37 C.F.R. 1.77 or to otherwiseprovide organizational cues. These headings shall not limit orcharacterize the invention(s) set out in any claims that may issue fromthis disclosure. Specifically and by way of example, although theheadings might refer to a “Field,” the claims should not be limited bythe language chosen under this heading to describe the so-called field.Further, a description of a technology in the “Background” is not to beconstrued as an admission that certain technology is prior art to anyinvention(s) in this disclosure. Neither is the “Summary” to beconsidered as a limiting characterization of the invention(s) set forthin issued claims. Furthermore, any reference in this disclosure to“invention” in the singular should not be used to argue that there isonly a single point of novelty in this disclosure. Multiple inventionsmay be set forth according to the limitations of the multiple claimsissuing from this disclosure, and such claims accordingly define theinvention(s), and their equivalents, that are protected thereby. In allinstances, the scope of the claims shall be considered on their ownmerits in light of this disclosure, but should not be constrained by theheadings set forth herein.

Use of broader terms such as comprises, includes, and having should beunderstood to provide support for narrower terms such as consisting of,consisting essentially of, and comprised substantially of. Use of theterm “optionally,” “may,” “might,” “possibly,” and the like with respectto any element of an embodiment means that the element is not required,or alternatively, the element is required, both alternatives beingwithin the scope of the embodiment(s). Also, references to examples aremerely provided for illustrative purposes, and are not intended to beexclusive.

While preferred embodiments have been shown and described, modificationsthereof can be made by one skilled in the art without departing from thescope or teachings herein. The embodiments described herein areexemplary only and are not limiting. Many variations and modificationsof the systems, apparatus, and processes described herein are possibleand are within the scope of the disclosure. For example, the relativedimensions of various parts, the materials from which the various partsare made, and other parameters can be varied. Accordingly, the scope ofprotection is not limited to the embodiments described herein, but isonly limited by the claims that follow, the scope of which shall includeall equivalents of the subject matter of the claims. Unless expresslystated otherwise, the steps in a method claim may be performed in anyorder. The recitation of identifiers such as (a), (b), (c) or (1), (2),(3) before steps in a method claim are not intended to and do notspecify a particular order to the steps, but rather are used to simplifysubsequent reference to such steps.

Also, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as directly coupled or communicating witheach other may be indirectly coupled or communicating through someinterface, device, or intermediate component, whether electrically,mechanically, or otherwise. Other examples of changes, substitutions,and alterations are ascertainable by one skilled in the art and could bemade without departing from the spirit and scope disclosed herein.

We claim:
 1. A method of identifying inflow locations along a wellbore,the method comprising: obtaining an acoustic signal from a sensor withinthe wellbore, wherein the acoustic signal comprises acoustic samplesacross a portion of a depth of the wellbore; determining a plurality offrequency domain features from the acoustic signal, wherein theplurality of frequency domain features are obtained across a pluralityof depth intervals within the portion of the depth of the wellbore; andidentifying, using a plurality of fluid flow models, a presence of atleast one of a gas phase inflow, an aqueous phase inflow, or ahydrocarbon liquid phase inflow at one or more fluid flow locations,wherein each fluid flow model of the plurality of fluid inflow modelsuses one or more frequency domain features of the plurality of thefrequency domain features, and wherein at least two of the plurality offluid flow models are different.
 2. The method of claim 1, wherein theplurality of frequency domain features comprises at least two differentfrequency domain features.
 3. The method of claim 1, wherein the one ormore fluid flow locations comprise one or more fluid inflow locations,wherein the method further comprises: identifying the one or more fluidinflow locations within the plurality of depth intervals using one ormore frequency domain features of the plurality of frequency domainfeatures.
 4. The method of claim 1, wherein the plurality of frequencydomain features comprises at least two of: a spectral centroid, aspectral spread, a spectral roll-off, a spectral skewness, a root meansquare (RMS) band energy, a total RMS energy, a spectral flatness, aspectral slope, a spectral kurtosis, a spectral flux, a spectralautocorrelation function, or a normalized variant thereof.
 5. The methodof claim 1, further comprising: denoising the acoustic signal prior todetermining the plurality of frequency domain features, whereindenoising the acoustic signal comprises median filtering the acousticdata.
 6. The method of claim 1, further comprising: normalizing the oneor more frequency domain features prior to identifying the one or moreinflow locations using the one or more frequency domain features.
 7. Themethod of claim 1, wherein identifying the one or more fluid inflowlocations comprises: identifying one or more anomalies in the acousticsignal using the one or more frequency domain features of the pluralityof frequency domain features; and selecting the depth intervals of theone or more anomalies as the one or more inflow locations.
 8. The methodof claim 1, wherein identifying at least one of the gas phase inflow,the aqueous phase inflow, or the hydrocarbon liquid phase inflowcomprises: wherein the plurality of fluid flow models comprise aplurality of logistic regression models for each of the gas phaseinflow, the aqueous phase inflow, and the hydrocarbon liquid phaseinflow; and determining that at least one of the gas phase inflow, theaqueous phase inflow, or the hydrocarbon liquid phase inflow is presentbased on the plurality of logistic regression models.
 9. The method ofclaim 8, wherein the plurality of logistic regression models use a firstmultivariate model having the plurality of frequency domain features asinputs to determine when the gas phase inflow is present, wherein theplurality of logistic regression models use a second multivariate modelhaving the plurality of frequency domain features as inputs to determinewhen the aqueous phase inflow is present, and wherein the plurality oflogistic regression models use a third multivariate model having theplurality of frequency domain features as inputs to determine when thehydrocarbon liquid phase inflow is present.
 10. The method of claim 1,further comprising: determining an amplitude of each of the identifiedat least one of the gas phase inflow, the aqueous phase inflow, or thehydrocarbon liquid phase inflow over a time period; and determining arelative contribution to a volumetric flow of each of the gas phaseinflow, the aqueous phase inflow, or the hydrocarbon liquid phase inflowbased on the amplitude of each of the identified at least one of the gasphase inflow, the aqueous phase inflow, or the hydrocarbon liquid phaseinflow over the time period.
 11. The method of claim 10, furthercomprising: determining a remediation procedure based on the relativecontribution of each of the gas phase inflow, the aqueous phase inflow,or the hydrocarbon liquid phase inflow; and performing the remediationprocedure.
 12. The method of claim 8, wherein the plurality of frequencydomain features comprise a normalized variant of the spectral spread anda normalized variant of the spectral centroid, and wherein the pluralityof logistic regression models define a relationship between a presenceor absence of the gas phase inflow, the aqueous phase inflow, or thehydrocarbon liquid phase inflow.
 13. The method of claim 1, furthercomprising: determining a confidence level for the identification of theat least one of the gas phase inflow, the aqueous phase inflow, or thehydrocarbon liquid phase inflow; and performing a remediation procedurebased on the confidence level.
 14. The method of claim 1, whereinidentifying the at least one of the gas phase inflow, the aqueous phaseinflow, or the hydrocarbon liquid phase inflow using the plurality ofthe frequency domain features comprises: identifying the at least one ofthe gas phase inflow, the aqueous phase inflow, or the hydrocarbonliquid phase inflow using at least one of: transformations of at leastone of the plurality of the frequency domain features, or a ratiobetween at least two of the plurality of the frequency domain features.15. The method of claim 1, further comprising: identifying, using theplurality of fluid flow models, a presence of at least one of a gasphase flow, an aqueous phase flow, or a hydrocarbon liquid flow;identifying a fluid flow regime based on the identifying of the at leastone of the gas phase inflow, the aqueous phase inflow, the hydrocarbonliquid phase inflow, the gas phase flow, the aqueous phase flow, or thehydrocarbon liquid flow using the plurality of the frequency domainfeatures at the one or more fluid flow locations.
 16. A method ofdeveloping a fluid flow model for a wellbore, the method comprising:performing a plurality of flow tests, wherein each flow test comprisesintroducing one or more fluids of a plurality of fluids into a flowingfluid within a conduit at predetermined locations, wherein the pluralityof fluids comprise a hydrocarbon gas, a hydrocarbon liquid, an aqueousfluid, or a combination thereof; obtaining an acoustic signal from asensor within the conduit for each flow test of the plurality of flowtests, wherein the acoustic signal comprises acoustic samples across aportion of the conduit including the predetermined locations;determining a plurality of frequency domain features from the acousticsignal for each of the plurality of fluid flow tests, wherein theplurality of frequency domain features are obtained across the portionof the conduit including the predetermined locations; and training aplurality of fluid flow models using the plurality of frequency domainfeatures for the plurality of the tests and the predetermined locations,where a first fluid flow model of the plurality of fluid flow models isdifferent than a second fluid flow model of the plurality of fluid flowmodels.
 17. The method of claim 16, further comprising: validating theplurality of fluid flow models using the acoustic signals from one ormore of the tests and the predetermined locations of the plurality oftests.
 18. The method of claim 16, wherein the conduit comprises a flowloop, and wherein the flowing fluid comprises an aqueous fluid, ahydrocarbon fluid, a gas, or a combination thereof, or wherein theflowing fluid comprises a liquid phase, a multi-phase mixed liquid, or aliquid-gas mixed phase.
 19. The method of claim 16, wherein theplurality of fluid flow models comprises logistic regression models, andwherein training the plurality of fluid flow models comprises: providingthe one or more frequency domain features to a first logistic regressionmodel of the logistic regression models corresponding to one or moreflow tests of the plurality of inflow tests where the one or more fluidscomprise a hydrocarbon gas; providing the one or more frequency domainfeatures to the first logistic regression model corresponding to one ormore flow tests of the plurality of inflow tests where the one or morefluids do not comprise a hydrocarbon gas; and determining a firstmultivariate model using the one or more frequency domain features asinputs, wherein the first multivariate model defines a relationshipbetween a presence and an absence of the hydrocarbon gas in the one ormore fluids.
 20. The method of claim 19, wherein training the pluralityof fluid flow models comprises: providing the one or more frequencydomain features to the second logistic regression model of the logisticregression models corresponding to one or more flow tests of theplurality of inflow tests where the one or more fluids comprise anaqueous fluid; providing the one or more frequency domain features tothe second logistic regression model corresponding to one or more flowtests of the plurality of inflow tests where the one or more fluids donot comprise a aqueous fluid; and determining a second multivariatemodel using the one or more frequency domain features as inputs, whereinthe second multivariate model defines a relationship between a presenceand an absence of the aqueous fluid in the one or more fluids.
 21. Themethod of claim 20, wherein training the plurality of fluid flow modelscomprises: providing the one or more frequency domain features to athird logistic regression model of the logistic regression modelscorresponding to one or more flow tests of the plurality of inflow testswhere the one or more fluids comprise a hydrocarbon liquid; providingthe one or more frequency domain features to the third logisticregression model corresponding to one or more flow tests of theplurality of inflow tests where the one or more fluids do not comprise ahydrocarbon liquid; and determining a third multivariate model using theone or more frequency domain features as inputs, wherein the thirdmultivariate model defines a relationship between a presence and anabsence of the hydrocarbon liquid in the one or more fluids.
 22. Themethod of claim 16, wherein the one or more frequency domain featurescomprise a normalized variant of the spectral spread (NVSS) and anormalized variant of the spectral centroid (NVSC).
 23. The method ofclaim 22, further comprising: providing the acoustic signals from one ormore of the plurality of flow tests and the predetermined locations ofthe one or more of the plurality of flow tests to each of the firstmultivariate model, the second multivariate model, and the thirdmultivariate model; determining a presence or absence of at least one ofthe gas in the one or more fluids, the aqueous fluid in the one or morefluids, or the hydrocarbon liquid in the one or more fluids based on anoutput of each of the first multivariate model, the second multivariatemodel, and the third multivariate model; and validating the fluid flowmodel using at least a portion of the plurality of flow tests, thepredetermined locations of the plurality of inflow tests, and thepresence of at least one of the gas in the one or more fluids, theaqueous fluid in the one or more fluids, or the hydrocarbon liquid inthe one or more fluids as determined from the first multivariate model,the second multivariate model, and the third multivariate model.
 24. Themethod of claim 16, wherein the plurality of fluid flow models isdeveloped using a supervised learning algorithm.
 25. A method ofcharacterizing fluid inflow into a wellbore, the method comprising:obtaining an acoustic signal from a sensor within the wellbore, whereinthe acoustic signal comprises acoustic samples across a portion of adepth of the wellbore; determining a plurality of frequency domainfeatures from the acoustic signal, wherein the plurality of frequencydomain features are obtained across a plurality of depth intervalswithin the portion of the depth of the wellbore, and wherein theplurality of frequency domain features comprise at least two differentfrequency domain features; identifying one or more fluid inflowlocations within the plurality of depth intervals using one or morefrequency domain features of the plurality of frequency domain features;providing the plurality of frequency domain features at the identifiedone or more fluid inflow locations to a plurality of fluid flow models;and determining at least one of a gas phase inflow, an aqueous phaseinflow, or a hydrocarbon liquid phase inflow at the identified one ormore fluid inflow locations using the plurality of fluid flow models,and wherein at least two of the plurality of fluid flow models usedifferent parameters.
 26. The method of claim 25, further comprising:determining an amplitude of each of the determined at least one of thegas phase inflow, the aqueous phase inflow, or the hydrocarbon liquidphase inflow over a time period at the identified one or more fluidinflow locations; and determining a relative contribution to avolumetric flow of each of the gas phase inflow, the aqueous phaseinflow, or the hydrocarbon liquid phase inflow based on the amplitude ofeach of the identified at least one of the gas phase inflow, the aqueousphase inflow, or the hydrocarbon liquid phase inflow over the timeperiod.
 27. The method of claim 25, wherein the plurality of fluid flowmodels comprises a plurality of logistic regression models, and whereindetermining at least one of the gas phase inflow, the aqueous phaseinflow, or the hydrocarbon liquid phase using the fluid flow modelcomprises: providing the plurality of frequency domain features to theplurality of logistic regression models corresponding to each of the gasphase inflow, the aqueous phase inflow, and the hydrocarbon liquid phaseinflow; and determining that at least one of the gas phase inflow, theaqueous phase inflow, or the hydrocarbon liquid phase inflow is presentbased on outputs from each of the plurality of logistic regressionmodels.
 28. The method of claim 27, wherein the plurality of logisticregression models uses a first multivariate model having a first subsetof the plurality of frequency domain features as inputs to determinewhen the gas phase inflow is present, wherein the plurality of logisticregression models uses a second multivariate model having a secondsubset of the plurality of frequency domain features as inputs todetermine when the aqueous phase inflow is present, and wherein theplurality of logistic regression models uses a third multivariate modelhaving a third subset of the plurality of frequency domain features asinputs to determine when the hydrocarbon liquid phase inflow is present.29. The method of claim 28, wherein determining at least one of the gasphase inflow, the aqueous phase inflow, or the hydrocarbon liquid phaseusing the fluid flow model comprises: using the first subset of theplurality of frequency domain features at the identified one or morefluid inflow locations in the first multivariate model; using the secondsubset of the plurality of frequency domain features at the identifiedone or more fluid inflow locations in the second multivariate model;using the third subset of the plurality of frequency domain features atthe identified one or more fluid inflow locations in the thirdmultivariate model; comparing the plurality of frequency domain featuresto an output of the first multivariate model, an output of the secondmultivariate model, and an output of the third multivariate model; andidentifying at least one of the gas phase inflow, the aqueous phaseinflow, or the hydrocarbon liquid phase inflow based on the comparisonof the plurality of frequency domain features to the output of the firstmultivariate model, the output of the second multivariate model, and theoutput of the third multivariate model.
 30. The method of claim 25,further comprising: integrating a total acoustic energy from theacoustic signal for each identified gas phase inflow, aqueous phaseinflow, or hydrocarbon liquid phase inflow at each depth correspondingto the identified one or more fluid inflow locations; determine anamplitude of the total acoustic energy for each identified gas phaseinflow, aqueous phase inflow, or hydrocarbon liquid phase inflow at eachdepth corresponding to the identified one or more fluid inflowlocations; and determine a volume of each gas phase inflow, aqueousphase inflow, or hydrocarbon liquid phase inflow at each depthcorresponding to the identified one or more fluid inflow locations usingthe amplitude of the total acoustic energy.